can i do phd in artificial intelligence

Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2024 for a start date of August 2025 in the Machine Learning PhD program, GRE Scores are OPTIONAL. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2023, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2019, will not be accepted.)

Graduate Online Application

• Admissions application opens September 4, 2024
• Early Application Deadline – November 20, 2024 (3:00 p.m. EST)
• Final Application Deadline - December 11, 2024 (3:00 p.m. EST)

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MastersInAI.org

PhD in Artificial Intelligence Programs

On This Page:

Universities offer a variety of Doctor of Philosophy (Ph.D.) programs related to Artificial Intelligence (AI.) Some of these are titled as Ph.D.s in AI, whereas most are Ph.D.s in Computer Science or related engineering disciplines with a specialization or focus in AI. Admissions requirements usually include a related bachelor’s degree and, sometimes, a master’s degree. Moreover, most Ph.D. programs expect academic excellence and strong recommendations. The AI Ph.D. programs take three to five or more years, depending on if you have a master’s and the complexity of your dissertation. People with Ph.D.s in AI usually go on to tenure track professorships, postdoctoral research positions, or high-level software engineering positions.

What Are Artificial Intelligence Ph.D. Programs?

Ph.D. programs in AI focus on mastering advanced theoretical subjects, such as decision theory, algorithms, optimization, and stochastic processes. Artificial intelligence covers anything where a computer behaves, rationalizes, or learns like a human. Ph.D.s are usually the endpoint to a long educational career. By the time scholars earn Ph.D.s, they have probably been in school for well over 20 years.

People with an AI Ph.D. degree are capable of formulating and executing novel research into the subtopics of AI. Some of the subtopics include:

• Environment adaptation in self-driving vehicles
• Natural language processing in robotics
• Cheating detection in higher education
• Diagnosing and treating diseased in healthcare

AI Ph.D. programs require candidates to focus most of their coursework and research on AI topics. Most culminate in a dissertation of published research. Many AI Ph.D. recipients’ dissertations are published in peer-reviewed journals or presented at industry-leading conferences. They go on to lead careers as experts in AI technology.

Types of Artificial Intelligence Ph.D. Programs

Most AI Ph.D. programs are a Ph.D. in Computer Science with a concentration in AI. These degrees involve general, advanced level computer science courses for the first year or two and then specialize in AI courses and research for the remainder of the curriculum.

AI Ph.D.s offered in other colleges like Computer Engineering, Systems Engineering, Mechanical Engineering, or Electrical Engineering are similar to Ph.D.s in Computer Science. They often involve similar coursework and research. For instance, colleges like Indiana University Bloomington’s Computing and Engineering have departments specializing in AI or Intelligent Engineering. Some colleges, however, may focus more on a specific discipline. For example, a Ph.D. in Mechanical Engineering with an AI focus is more likely to involve electric vehicles than targeted online advertising.

Some AI programs fall under a Computational Linguistics specialization, like CUNY . These programs emphasize the natural language processing aspect of AI. Computational Linguistics programs still involve significant computer science and engineering but also require advanced knowledge in language and speech.

Other unique programs offer a joint Ph.D. with non-engineering disciplines, such as Carnegie Mellon’s Joint Ph.D. in Machine Learning and Public Policy, Statistics, or Neural Computation .

How Ph.D. in Artificial Intelligence Programs Work

Ph.D. programs usually take three to six years to complete. For example, Harvard lays out a three+ year track where the last year(s) is spent completing your research and defending your dissertation. Many Ph.D. programs have a residency requirement where you must take classes on-campus for one to three years. Moreover, most universities, such as Brandeis , require Ph.D. students to grade and/or teach for one to four semesters. Despite these requirements, several Ph.D. programs allow for part-time or full-time students, like Drexel .

Admissions Requirements

Ph.D. programs in AI admit the strongest students. Most applications require a resume, transcripts, letters of recommendation, and a statement of interest. Many programs require a minimum undergraduate GPA of 3.0 or higher, although some allow for statements of explanation if you have a lower GPA due to illness or other excusable causes for a low GPA.

Many universities, like Cornell , recently made the GRE either optional or not required because the GRE provides little prediction into the success of research and represents a COVID-19 risk. These programs may require the GRE again in the future. However, many schools still require the IELTS/TOEFL for international applicants.

Curriculum and Coursework

The curriculum for AI Ph.D.s varies based on the applicants’ prior education for many universities. Some programs allow applicants to receive credit for relevant master’s programs completed prior to admission. The programs require about 30 hours of advanced research and classes. Other programs do not give credit for master’s programs completed elsewhere. These require over 60 hours of electives, in addition to the 30-hours of fundamental and core classes in addition to the advanced courses.

For programs with more specific specialties, the courses are usually narrowly focused. For example, Duke’s Robotics track requires ten classes, at least three of which are focused on AI as it relates to robotics. Others allow for non-AI-specific courses such as computer networks.

Many Ph.D. programs have strict GPA requirements to remain in the program. For example, Northeastern requires PhD candidates to maintain at least a 3.5 GPA. Other programs automatically dismiss students with too many Cs in courses.

Common specializations include:

• Computational Linguistics
• Automotive Systems
• Data Science

Artificial Intelligence Dissertations

Most Ph.D. programs require a dissertation. The dissertation takes at least two years to research and write, usually starting in the second or third year of the Ph.D. curriculum. Moreover, many programs require an oral presentation or defense of the dissertation. Some universities give an award for the best dissertation of the year. For example, Boston University gave a best dissertation award to Hao Chen for the dissertation entitled “ Improving Data Center Efficiency Through Smart Grid Integration and Intelligent Analytics .”

A couple of programs require publications, like Capitol Technology , or additional course electives, like LIU . For example, The Ohio State University requires 27 hours of graded coursework and three hours with an advisor for non-thesis path candidates. Thesis-path candidates only have to take 18 hours of graded coursework but must spend 12 hours with their advisors.

Are There Online Ph.D. in Artificial Intelligence Programs?

Officially, the majority of AI Ph.D. programs are in-person. Only one university, Capitol Technology University , allows for a fully online program. This is one of the most expensive Ph.D.s in the field, costing about $60,000. However, it is also one of the most flexible programs. It allows you to complete your coursework on your own schedule, perhaps even while working. Moreover, it allows for either a dissertation path or a publication path. The coursework is fully focused on AI research and writing, thus eliminating requirements for more general courses like algorithms or networks.

One detail you should consider is that the Capitol Technology Ph.D. program is heavily driven by a faculty mentor. This is someone you will need consistent contact with and open communication. The website only lists the director, so there is a significant element of uncertainty on how the program will work for you. But doctoral candidates who are self-driven and have a solid idea of their research path have a higher likelihood of succeeding.

If you need flexibility in your Ph.D. program, you may find some professors at traditional universities will work with you on how you meet and conduct the research, or you may find an alternative degree program that is online. Although a Ph.D. program may not be officially online, you may be able to spend just a semester or two on campus and then perform the rest of the Ph.D. requirements remotely. This is most likely possible if the university has an online master’s program where you can take classes. For example, the Georgia Institute of Technology does not have a residency requirement, has an online master’s of computer science program , and some professors will work flexibly with doctoral candidates with whom they have a close relationship.

What Jobs Can You Get with a Ph.D. in Artificial Intelligence?

Many Ph.D. graduates work as tenure track professors at universities with AI classes. Others work as postdoc research scientists at universities. Both of these roles are expected to conduct research and publish, but professors have more of an expectation to teach, as well. Universities usually have a small number of these positions available. Moreover, postdoc research positions tend to only last for a limited amount of time.

Other engineers with AI-focused-Ph.D.s conduct research and do software development in the private sector at AI-intensive companies. For example, Google uses AI in many departments. Its assistant uses natural language processing to interface with users through voice. Moreover, Google uses AI to generate news feeds for users. Google, and other industry leaders, have a strong preference for engineers with Ph.D.s. This career path is often highly sought by new Ph.D. recipients.

Another private sector industry shifting to AI is vehicle manufacturing. For example, self-driving cars use significant AI to make ethical and legal decisions while operating. Another example is that electric vehicles use AI techniques to optimize performance and power usage.

Some AI Ph.D. recipients become c-suite executives, such as Chief Technology Officers (CTO). For example, Dr. Ted Gaubert has a Ph.D. in engineering and works as a CTO for an AI-intensive company. Another CTO, Dr. David Talby , revolutionized AI with a new natural language processing library, Spark. CTO positions in AI-focused companies often have decades of experience in the AI field.

How Much Do Ph.D. in Artificial Intelligence Programs Cost?

The tuition for many Ph.D. programs is paid through fellowships, graduate research assistantships, and teaching assistantships. For example, Harvard provides full support for Ph.D. candidates. Some programs mandate teaching or research to attend based on the assumption that Ph.D. candidates need financial assistance.

Fellowships are often reserved for applicants with an exceptional academic and research background. These are usually named for eminent alumni, professors, or other scholars associated with the university. Receiving such a fellowship is a highly respected honor.

For programs that do not provide full assistance, the usual cost is about $500 to $1,000 per credit hour, plus university fees. On the low end, Northern Illinois University charges about $557 per credit hour . With 30 to 60 hours required, this means the programs cost about $30,000 to over $60,000 out of pocket. Typically, Ph.D. programs that do not provide funding for any Ph.D. candidates are less reputable or provide other benefits, such as flexibility, online programs, or fewer requirements.

How Much Does a Ph.D. in AI Make?

Engineers with AI Ph.D.s earn well into the six-figure range in the private sector. For example, OpenAI , a non-profit, pays its top researchers over $400,000 per year. Amazon pays its data scientists with Ph.D.s over $200,000 in salary. Directors and executives with Ph.D.s often earn over $1,000,000 in private industry.

When considering working in the private industry, professionals usually compare offers based on total compensation, not just salary. Many companies offer large stock and bonus packages to Ph.D.-level engineers and scientists.

Startups sometimes pay less in salary, but much more in stock options. For example, the salary may be $50,000 to $100,000, but when the startup goes public, you may end up with hundreds of thousands in stock options. This creates a sense of ownership and investment in the success of the startup.

Computer science professors and postdoctoral researchers earn about $90,000 to $160,000 from universities. However, they increase their competition by writing books, speaking at conferences, and advising companies. Startups often employ professors for advice on the feasibility and design of their technology.

Schools with PhD in Artificial Intelligence Programs

Arizona state university.

School of Computing and Augmented Intelligence

Tempe, Arizona

Ph.D. in Computer Science (Artificial Intelligence Research)

Ph.d. in computing and information sciences (artificial intelligence research), university of california-riverside.

Department of Electrical and Computer Engineering

Riverside, California

Ph.D. in Electrical Engineering - Intelligent Systems Research Area

University of california-san diego.

Electrical and Computer Engineering Department

La Jolla, California

Ph.D. in Intelligent Systems, Robotics and Control

Colorado state university-fort collins.

The Graduate School

Fort Collins, Colorado

Ph.D. in Computer Science - Artificial Intelligence Research Area

University of colorado boulder.

Paul M. Rady Mechanical Engineering

Boulder, Colorado

PhD in Robotics and Systems Design

District of columbia, georgetown university.

Department of Linguistics

Washington, District of Columbia

Doctor of Philosophy (Ph.D.) in Linguistics - Computational Linguistics

The university of west florida.

Department of Intelligent Systems and Robotics

Pensacola, Florida

Ph.D. in Intelligent Systems and Robotics

University of central florida.

Department of Electrical & Computer Engineering

Orlando, Florida

Doctorate in Computer Engineering - Intelligent Systems and Machine Learning

Georgia institute of technology.

Colleges of Computing, Engineering, and Sciences

Atlanta, Georgia

Ph.D. in Machine Learning

Northern illinois university.

Dekalb, Illinois

Ph.D. in Computer Science - Artificial Intelligence Area of Emphasis

Ph.d. in computer science - machine learning area of emphasis, northwestern university.

McCormick School of Engineering

Evanston, Illinois

PhD in Computer Science - Artificial Intelligence and Machine Learning Research Group

Indiana university bloomington.

Department of Intelligent Systems Engineering

Bloomington, Indiana

Ph.D. in Intelligent Systems Engineering

Ph.d. in linguistics - computational linguistics concentration, capitol technology university.

Doctoral Programs Department

Laurel, Maryland

Doctor of Philosophy (PhD) in Artificial Intelligence

Offered Online

Johns Hopkins University

Whiting School of Engineering

Baltimore, Maryland

Doctor of Philosophy in Mechanical Engineering - Robotics

Massachusetts, boston university.

College of Engineering

Boston, Massachusetts

PhD in Computer Engineering - Data Science and Intelligent Systems Research Area

Phd in systems engineering - automation, robotics, and control, brandeis university.

Department of Computer Science

Waltham, Massachusetts

Ph.D. in Computer Science - Computational Linguistics

Harvard university.

School of Engineering and Applied Sciences

Cambridge, Massachusetts

Ph.D. in Applied Mathematics

Northeastern university.

Khoury College of Computer Science

Ph.D. in Computer Science - Artificial Intelligence Area

University of michigan-ann arbor.

Electrical Engineering and Computer Science Department

Ann Arbor, Michigan

PhD in Electrical and Computer Engineering - Robotics

University of nebraska at omaha.

College of Information Science & Technology

Omaha, Nebraska

PhD in Information Technology - Artificial Intelligence Concentration

University of nevada-reno.

Computer Science and Engineering Department

Reno, Nevada

Ph.D. in Computer Science & Engineering - Intelligent and Autonomous Systems Research

Rutgers university.

New Brunswick, New Jersey

Ph.D. in Linguistics with Computational Linguistics Certificate

Stevens institute of technology.

Schaefer School Of Engineering & Science

Hoboken, New Jersey

Ph.D. in Computer Engineering

Ph.d. in electrical engineering - applied artificial intelligence, ph.d. in electrical engineering - robotics and smart systems research, cornell university.

Ithaca, New York

Linguistics Ph.D. - Computational Linguistics

Ph.d.in computer science, cuny graduate school and university center.

New York, New York

Ph.D. in Linguistics - Computational Linguistics

Long island university-brooklyn campus.

Graduate Department

Brooklyn, New York

Dual PharmD/M.S. in Artificial Intelligence

Rochester institute of technology.

Golisano College of Computing and Information Sciences

Rochester, New York

North Carolina

Duke university.

Duke Robotics

Durham, North Carolina

Ph.D in ECE - Robotics Track

Ph.d. in mems - robotics track, ohio state university-main campus.

Department of Mechanical and Aerospace Engineering

Columbus, Ohio

PhD in Mechanical Engineering - Automotive Systems and Mobility (Connected and Automated Vehicles)

University of cincinnati.

College of Engineering and Applied Science

Cincinnati, Ohio

PhD in Computer Science and Engineering - Intelligent Systems Group

Oregon state university.

Corvallis, Oregon

Ph.D. in Artificial Intelligence

Pennsylvania, carnegie mellon university.

Machine Learning Department

Pittsburgh, Pennsylvania

PhD in Machine Learning & Public Policy

Phd in neural computation & machine learning, phd in statistics & machine learning, phd program in machine learning, drexel university.

Philadelphia, Pennsylvania

Doctorate in Mechanical Engineering - Robotics and Autonomy

Temple university.

Computer & Information Sciences Department

PhD in Computer and Information Science - Artificial Intelligence

University of pittsburgh-pittsburgh campus.

School of Computing and Information

Ph.D. in Intelligent Systems

The university of texas at austin.

Austin, Texas

Ph.D. with Graduate Portfolio Program in Robotics

The university of texas at dallas.

Erik Jonsson School of Engineering and Computer Science

Richardson, Texas

University of Utah

Mechanical Engineering Department

Salt Lake City, Utah

Doctor of Philosophy - Robotics Track

University of washington-seattle campus.

Seattle, Washington

Ph.D. in Machine Learning and Big Data

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Online Doctor of Engineering in Artificial Intelligence & Machine Learning

We are now accepting applications for the cohort beginning in January 2025.

The application deadline is November 1, 2024

Program Description

The online Doctor of Engineering in Artificial Intelligence & Machine Learning is a research-based doctoral program. The program is designed to provide graduates with a solid understanding of the latest AI&ML techniques, as well as hands-on experience in applying these techniques to real-world problems. Graduates of this program are equipped to lead AI&ML projects and teams in a wide range of industries, including healthcare, finance, and manufacturing. Having developed advanced research skills, graduates are also well-prepared for academic research and teaching roles.

The degree requires completion of eight graduate-level courses (listed below) and a minimum of 24 credit hours of Praxis Research (SEAS 8588). During the research phase, the student writes and defends a research praxis on a topic related to AI&ML. The topic is selected by the student and approved by the research advising committee.

SEAS 6414 Python Application for Data Analytics:  Introduction to Python programming tailored for Data Analytics. This course covers Python’s applications in automating data cleaning, feature engineering, outlier detection, implementing machine learning algorithms, conducting text mining, and performing time series analysis. (3 credit hours)

SEAS 8500 Fundamentals of AI-Enabled Systems:  Operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. Systems perspective in designing AI systems. Full-lifecycle of creating AI-enabled systems. Ethics and biases in AI systems (3 credit hours)

SEAS 8505 Applied Machine Intelligence and Reinforcement Learning:  Theory and practice of machine learning leveraging open-source tools, algorithms and techniques. Topics include intelligent model training, support vector machines, deep learning, transformer methods, GANs, and reinforcement learning (3 credit hours)

SEAS 8510 Analytical Methods for Machine Learning:  Mathematical tools for building machine learning algorithms: linear algebra, analytical geometry, matrix decompositions, optimization, probability and statistics (3 credit hours)

SEAS 8515 Data Engineering for AI:  Developing Python scripts to automate data pipelines, data ingestion, data processing, and data warehousing. Machine learning applications with Python including text mining and time series analysis (3 credit hours)

SEAS 8520 Deep Learning and Natural Language Processing:  Fundamentals of deep learning and Natural Language Processing (NLP). Techniques for designing modern deep learning networks using Keras and TensorFlow. NLP topics include sentiment analysis, bag of words, TFIDF, and Large Language Models (3 credit hours)

SEAS 8525 Computer Vision and Generative AI: Explore AI's visual realm. Learn image processing object detection, and models in generative adversarial networks and neural networks. Master tools for creating AI applications in art, design, ethical considerations, and societal impacts of generative AI technology (3 credit hours)

SEAS 8599 Praxis Development for AI & Machine Learning:  Overview of research methods. Aims and purpose of the praxis. Development of praxis research strategies, formulation, and defense of a praxis proposal (3 credit hours)

SEAS 8588 Praxis Research for D.Eng. in AI & Machine Learning:  Research leading to the degree of Doctor of Engineering in AI and Machine Learning (24 Credit Hours)  

Classroom courses last 10 weeks each and meet on Saturday mornings from 9:00 AM—12:10 PM and afternoons from 1:00—4:10 PM (all times Eastern). All classes meet live online through synchronous distance learning technologies (Zoom). All classes are recorded and available for viewing within two hours of the lecture. This program is taught in a cohort format in which students take all courses in lockstep. Courses cannot be taken out of sequence, live attendance at all class meetings is expected, and students must remain continuously enrolled. Leaves of absence are permitted only in the case of a medical or family emergency, or deployment to active military duty.  Please see below for the dates of our upcoming cohort.

Semester	Session	#Credit Hours	Tentative Dates
Spring 2025	1	6	January 4 — March 8, 2025
Spring 2025	2	6	March 22 — May 31, 2025
Summer 2025	-	6	June 14 — August 23, 2025
Fall 2025	1	6	September 6 — November 8, 2025

No classes on Thanksgiving, Christmas, New Year, Fourth of July,  and Memorial Day Weekends 

To proceed to the research phase, students must earn a grade point average of at least 3.2 in the eight classroom courses, and no grade below B-. Students are then registered for a minimum of 24 credit hours of SEAS 8588 Praxis Research: 3 ch in Fall 2025 (Session 2), 9 ch in Spring 2026, 3 ch in Summer 2026, and 9 ch in Fall 2026. Throughout the research phase, students develop the praxis under the guidance of a designated faculty advisor. Faculty research advisors are assigned by the program office and meet individually with students every two weeks.

Sample research areas are listed below:

•    Developing algorithms and methods that can explain how AI systems reach their decisions or predictions, making them more transparent and trustworthy •    Investigating how reinforcement learning can improve robotic performance and control, particularly in complex environments •    Examining how to ensure that AI systems are fair and unbiased in their decision-making, particularly in areas such as hiring, lending, and criminal justice •    Developing more advanced natural language processing models and algorithms that can understand and interpret human language more accurately and effectively •    Investigating how to apply transfer learning techniques to improve the performance of AI systems in new and different domains, with less data and less training time 

Tuition is $1,750 per credit hour for the 2024-2025 year and is billed at the beginning of each semester for the courses registered during that semester. A non-refundable tuition deposit of $995, which is applied to tuition due the first semester, is required when the applicant accepts the offer of admission.

Admissions Process

• Bachelor’s and master’s degrees in engineering, applied science, business, computer science, or a related field from accredited institutions.
• A minimum graduate-level GPA of 3.2
• Capacity for original scholarship.
• TOEFL, IELTS, Duolingo, or PTE scores are required of all applicants who are not citizens of countries where English is the official language.  Check our  International Students Page  to learn about the SEAS English language requirements and exemption policy. Test scores may not be more than two years old.

Note: GRE and GMAT scores are not required

Please note that our doctoral programs are highly selective; meeting minimum admissions requirements does not guarantee admission.  

• Attach up-to-date Resume 
• Attach Statement of Purpose – In an essay of 250 words or less, state your purpose in undertaking graduate study at The George Washington University. Describe your academic objectives, research interests, and career plans. Discuss your qualifications including collegiate, professional, and community activities, and any other substantial accomplishments not mentioned.
• Online Engineering Programs The George Washington University 170 Newport Center Drive Suite 260 Newport Beach, CA 92660

Normally, all transcripts must be received before an admission decision is rendered for the Doctor of Engineering program. 

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PhD Programs in Artificial Intelligence 

The ultimate graduate degree in artificial intelligence is a doctoral degree, also referred to as a Doctor of Philosophy or PhD. Many PhDs in artificial intelligence are in computer science or engineering with a specialization or research focus in artificial intelligence. PhD students will learn advanced subjects in the discipline, like algorithms, decision theory, stochastic processes, and optimization. 

What are PhD Programs in Artificial Intelligence?

As mentioned above, PhD programs in artificial intelligence are usually PhDs in computer science with a concentration in artificial intelligence. There are other PhDs with specializations in artificial intelligence, as you will see in the titles of many of the degrees listed below. These may include Mechanical Engineering, Machine Learning, and Computational Linguistics.

Most classes in a PhD program will be on-campus, but there may be a few offered online. (Only one PhD program in AI is offered completely online, as you will see in the list below). Some colleges and universities require PhD students to attend school full-time, while others admit part-time students.

Cost  According to EducationData.org, the cost of a Doctor of Philosophy averages $106,860 as of 2023. Programs vary widely in their cost, however, depending upon whether the university is public or private and how much financial assistance is available.

Who Should Study for a PhD in Artificial Intelligence?

Those who get PhDs in artificial intelligence are often interested in research positions, teaching, or software engineering. 

Jobs that are commonly held by those with PhDs in artificial intelligence include, but are not limited to:

• Research scientists
• Software engineers
• Executives in business (Chief Technology Officer, etc.)
• Principal scientist
• Lead data scientist
• Machine learning engineer
• Computer scientist

According to Payscale.com, the average salary for a person holding a PhD in Artificial Intelligence as of 2023 is $115,000 annually. The highest salaries are earned by executives such as Chief Technology Officers holding PhDs in AI, who average $198,235 annually. Computer scientists with PhDs in artificial intelligence fall at the lower end of the salary range, making $91,926 per year.

What are the Admissions Requirements for PhD Programs in Artificial Intelligence? 

In order to be admitted to a PhD program in artificial intelligence, you will need at least a bachelor’s degree or a master’s degree in a related field. You must provide a resume, transcripts, a solid academic background, a personal statement of interest, and strong personal and professional recommendations. The Graduate Record Examination (GRE) may or may not be required. Check with the school(s) in which you are interested in applying for their GRE policies for doctoral students.

Be prepared for a long period of study, as the average PhD program takes three to six years to complete.  Keep in mind that, once admitted, many doctoral programs in AI will require you to maintain a 3.5 GPA in order to remain in the program. 

What is Studied in a PhD Program in Artificial Intelligence?

Courses that you will study in a PhD program in artificial intelligence will vary depending upon the specialization you choose, as well as your prior education. Some schools will give you credit for your master’s degree classes, while others will not. Expect courses in algorithms, analytics, statistics, machine learning, and robotics, among others.

Dissertation

The dissertation is the culmination of your doctoral degree studies, and shows the published research you have conducted on artificial intelligence and/or your specialization. It will take about two years to research and write a dissertation, and you will start working on it during the second or third year of your PhD program. 

Specializations

Potential specializations in PhD programs in AI are listed in the degrees below. They include, but are not limited to:

• Data Science
• Automotive Systems
• Computational Linguistics
• Machine Learning
• Autonomous Systems

Doctoral Programs in Artificial Intelligence 

Below you will find a list of PhD programs in various fields of artificial intelligence across the U.S., as of 2023.

PhD. Programs in Computational Linguistics and Natural Language Processing

District of Columbia

• Doctor of Philosophy in Linguistics: Computational Linguistics
•  Doctor of Philosophy in Linguistics: Concentration in Computational Linguistics

Massachusetts

• Doctor of Philosophy in Computer Science with Studies in Computational Linguistics
• Doctor of Philosophy in Linguistics: Computational Methods

New York 

• Doctor of Philosophy in Linguistics : Computational Linguistics
• Doctor of Philosophy in Linguistics : Concentration in Computational Linguistics
• D octor of Philosophy in Linguistics-Computational Linguistics track

Doctoral Programs in Machine Learning

• Doctor of Philosophy in Computer Engineering: Intelligent Systems and Machine Learning
• Doctor of Philosophy in Machine Learning
• Doctor of Philosophy in Computer Science: Artificial Intelligence and Machine Learning
• Doctor of Philosophy in Computer Science: Machine Learning emphasis
• Doctor of Philosophy in Applied Mathematics: Machine Learning and Artificial Intelligence concentration

Pennsylvania

• Doctor of Philosophy in Statistics & Machine Learning
• Doctor of Philosophy in Machine Learning & Public Policy
• Doctor of Philosophy in Neural Computation & Machine Learning
• Doctor of Philosophy in Statistics: Machine Learning and Big Data track

Doctoral Programs in Robotics and Autonomous Systems

• Doctor of Philosophy in Electrical & Computer Engineering: Intelligent Systems, Robotics and Control
• Doctor of Philosophy in Mechanical Engineering: Robotics and Systems Design
• Doctor of Philosophy in Intelligent Systems & Robotics
• D octor of Philosophy in Mechanical Engineering: Robotics
• Doctor of Philosophy in Systems Engineering: Automation, Robotics and Control
• Doctor of Philosophy in Human-Robot Interaction
• Doctor of Philosophy in Electrical and Computer Engineering: Robotics
• Doctor of Philosophy in Computer Science & Engineering- Intelligent & Autonomous Systems Research

New Jersey 

• Doctor of Philosophy in Electrical Engineering: Robotics and Smart Systems Research

North Carolina

• Doctor of Philosophy in Electrical and Computer Engineering: Signal and Information Processing Track: Robotics Certificate
• Doctor of Philosophy in Mechanical Engineering and Materials Science: Robotics Certificate
• Doctor of Philosophy in Mechanical Engineering: Automotive Systems and Mobility
• Offers BS-PhD and MS-PhD paths
• Doctor of Philosophy in Mechanical Engineering: Robotics and Autonomy

Texas 

• Doctor of Philosophy in Robotics
• Doctor of Philosophy in Mechanical Engineering: Robotics Track
• Doctor of Philosophy in Computer Science: Artificial Intelligence Research
• Doctor of Philosophy in Electrical Engineering: Intelligent Systems Research
• Doctor of Philosophy in Computer Science: Artificial Intelligence Emphasis
• Doctor of Philosophy in Intelligent Systems Engineering
• Doctor of Philosophy in Artificial Intelligence
• Online program
• Doctor of Philosophy in Computer Engineering: Data Science and Intelligent Systems Research Area
• Doctor of Philosophy in Computer Science: Artificial Intelligence
• Doctor of Philosophy in Information Technology: Artificial Intelligence concentration
• Doctor of Philosophy in Computer Engineering
• Doctor of Philosophy in Electrical Engineering: Applied Artificial Intelligence
• Pharm.D. and Master of Science in Artificial Intelligence
• Doctor of Philosophy in Computing and Information Sciences: Artificial Intelligence Research

Oregon 

• Doctor of Philosophy in Computer and Information Science: Artificial Intelligence
• Doctor of Philosophy in Intelligent Systems
• Doctor of Philosophy in Computing and Information Sciences – Artificial Intelligence Research

• Doing a PhD in Artificial Intelligence

Artificial intelligence and intelligent systems are thought to be the key to the next ‘industrial revolution’. In a data-rich world, developing an artificial intelligence which can learn from its experience and call on human behaviour to make decisions could change the way we live and offer endless economic, social and scientific applications. Nationwide, there is an increasing demand for AI workers, as the world is becoming more reliant on developing technology and automated systems. Consequently, more and more people are pursuing postgraduate research in AI.

What Does a PhD in Artificial Intelligence Focus On?

A PhD in artificial intelligence will give you a deep understanding of AI, allow you to contribute to the development of emerging technology, and equip you with highly applicable technical skills. For example, engineering applications of artificial intelligence include automation of tasks and parametric modelling. Medical applications include using data-science approaches to identify patterns of illness in clinical data. Financial applications include using machine learning platforms to crunch huge amounts of data and help credit lenders in analysing risk and assess potential borrowers.

Artificial Intelligence PhD programme can focus on:

• Machine learning
• Deep learning
• Natural language processing
• Deep neural network architecture
• Human-machine interaction
• Augmented reality
• And countless other areas

Browse PhDs in Artificial Intelligence

Application of artificial intelligence to multiphysics problems in materials design, from text to tech: shaping the future of physics-based simulations with ai-driven generative models, study of the human-vehicle interactions by a high-end dynamic driving simulator, coventry university postgraduate research studentships, discovery of solid state electrolytes using deep learning, entry requirements for a phd in artificial intelligence.

The entry requirements for a PhD in AI are typically an upper second-class honours degree (or international equivalent) in a relevant subject from an accredited university. Subjects considered relevant to artificial intelligence include computer science, engineering, mathematics, statistics, electronics/electrical engineering or science.

Some research courses also require applicants to possess experience in programming, the desired programming language will be specific to the research project. Academic or work experience in machine learning or data science are typically favourable for applications.

International students will also need to meet several minimum English language requirements set by the university, usually as part of a TOEFL or IELTS exam.

Duration and Programme Types

Like most PhDs, a doctoral programme in Artificial Intelligence typically takes 3-4 years full-time, or 6 years part-time .

Aside from the traditional PhD, there is also the CDT PhD. Many Universities have Centres for Doctoral Training (CDTs) which are often funded by the UKRI centre . These CDTs can offer the CDT PhD which is a specialised PhD programme in artificial intelligence. The main difference between a standard PhD in artificial intelligence and a CDT PhD in artificial intelligence is that the latter includes additional modules which give candidates training in neuroscience, entrepreneurship, high performance computing, AI ethics and science communication.

Due to the different research areas you can pursue within the artificial intelligence field, the nature of programmes can vary. Some PhD research programmes are computational based and heavily reliant on coding, mathematics and lab work. Other research programmes can be people facing, involving questionnaires, for example to determine public perception on proposed legislation.

Costs and Funding

Annual tuition fees for PhDs in Artificial Intelligence are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £25,000 per academic year.

A variety of scholarships and funding support options are available for postgraduate study. For Artificial Intelligence in particular, the UKRI and ESPRC offer a number of studentships and CDT opportunities across varies universities. Many universities have research centres which are partnered with UK research councils and offer fully funded programmes. Funding is generally available for UK/EU students. International students are also eligible for some funding opportunities, but these tend to be less widely available.

Available Career Paths in AI

One of the key advantages of Artificial Intelligence is that it has a wide range of applications, and hence there are many career paths available. As computer systems and data have become more integrated in everyday life, the demand for experts in AI has grown rapidly. This high demand has resulted in many high job security and lucrative salaries.

Examples destinations for an AI PhD student include:

Data Analyst – If you are very analytical research student, you may use your artificial intelligence PhD to pursue a career in data science or analysis. Data analysts can be found in engineering, finance, healthcare, and everywhere in between. They are responsible for data crunching and using their skills to present complex information in a clear manner – visually and orally. Typical duties include record management, maintaining automated processes, monitoring analytics and KPIs, improving algorithms, and creating dashboards for clients. The average salary for data analysts in the UK is around £30,000 – £45,000, though this number can increase drastically depending on the sector.

Cyber Security – As cyber-attacks are becoming more commonplace, industries are looking to develop their cyber security, and salaries are seeing a sharp increase accordingly. AI doctorates are well placed for a career in cyber security, and typical career destinations include security analysts, penetration testers, systems engineers, web developers and cybersecurity consultants. In these roles, you will be responsible for protecting IT infrastructure and help develop security systems.

Machine learning – Often those with a PhD in AI become machine learning engineers, responsible for the development of intelligent systems. Machine learning is a subset of AI which focuses on the idea that machines can be programmed to learn from data and experience to improve decision making without human input. Machine learning is perhaps at the forefront of AI research, and there are numerous programmes look to improve its capabilities. This is well suited for those who enjoy the mathematical and programming side of computer science. Typical duties include managing data pipelines, developing algorithms, liaising with stakeholders, analysing datasets, and leading software design. Entry level salaries are around £35,000 and can exceed £150,000 with experience. Deep learning is similar to machine learning, the main difference is that deep learning aims to create artificial ‘neural networks’.

Postgraduate research often leads to an academic career. As an academic you can propose your own research projects based on your interest and supervise students. As a professor you can shape the next generation of AI experts and as a researcher you can make use of a university’s department resources, facilities and industrial ties to work with cutting edge technology and push the boundaries of our knowledge.

Browse PhDs Now

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Course type

Qualification, university name, phd degrees in artificial intelligence (ai).

16 degrees at 13 universities in the UK.

Customise your search

Select the start date, qualification, and how you want to study

About Postgraduate Artificial Intelligence (AI)

Artificial Intelligence (AI) is a branch of computer science focused on creating machines that can perform tasks with a simulated human intelligence. AI builds computers which can learn, reason, problem-solve and understand natural language. It is a relatively new and rapidly advancing field with a huge range of practical applications from speech recognition and image processing to autonomous vehicles and virtual assistants.

Currently there are 15 artificial intelligencePhD programmes offered at UK universities and entry requirements typically include a strong background in computer science, software engineering or a related field, along with a well-constructed research proposal, which should address an important or underdeveloped aspect of AI and will form the basis for your PhD studies.

The PhD course itself will have a duration of around three to six years and will primarily be centered around your research proposal which you’ll develop under the supervision of an academic tutor.

What to Expect

For a PhD, you can expect to be doing a lot of self-driven study. You may be part of a research team or a member of a laboratory or engineering workshop, but a significant amount of your time will still be spent researching material for your thesis and developing your project. AI is a highly versatile field and you might find yourself working in machine learning, robotics, natural language processing, computational intelligence, or even the ethics behind striving to create intelligent machines and what effects this might have on human society.

You’ll present your research periodically; however, the main assessment is your PhD dissertation, which after submitting, you will be required to defend orally in front of a panel of academics. Once this is complete, you’ll be qualified as a Doctor of Philosophy in artificial intelligence and will be ready for roles in AI research, data science, industry or academia.

Related subjects:

• PhD Artificial Intelligence (AI)
• PhD Animation Software
• PhD Bioinformatics
• PhD Business Information Systems
• PhD Computer Animation
• PhD Computer Architectures
• PhD Computer Communications and Networking
• PhD Computer Cybernetics
• PhD Computer Games Design
• PhD Computer Graphics
• PhD Computer Network Components
• PhD Computer Science and Information Technology
• PhD Computer Security Systems
• PhD Computer Systems
• PhD Computing Methodologies
• PhD Data Science
• PhD Expert Systems
• PhD Geographical Information Systems Software
• PhD Graphics And Multimedia Software
• PhD Health Informatics
• PhD Human Computer Interface Development
• PhD Image Processing
• PhD Informatics
• PhD Information Management
• PhD Information Security
• PhD Information Systems
• PhD Information Technology
• PhD Information Work and Information Use
• PhD Internet Security Systems
• PhD Internet Systems
• PhD Knowledge Management Systems
• PhD Librarianship and Library Management
• PhD Libraries and Librarianship
• PhD Mobile Computing
• PhD Modelling and Simulation Systems
• PhD Multimedia
• PhD Network Systems Management
• PhD Network Systems Management Software
• PhD Pattern Recognition
• PhD Software Development
• PhD Software Engineering
• PhD Software Testing
• PhD Software for Specific Subjects and Industries
• PhD Systems Analysis and Design
• PhD Using Software

• Course title (A-Z)
• Course title (Z-A)
• Price: high - low
• Price: low - high

PhD Artificial Intelligence and Music

Queen mary university of london.

The UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM) is a leading PhD research programme aimed at the Read more...

• 3 years Full time degree: £4,786 per year (UK)
• 6 years Part time degree: £2,393 per year (UK)

PhD Robotics and Systems Engineering

University of salford.

INTRODUCTION Automation for the Food Industry Research The food industry is very labour intensive and as a result is under threat from Read more...

• 5 years Online/Distance degree: £2,395 per year (UK)
• 3 years Online/Distance degree: £4,786 per year (UK)

Robotics and Autonomous Systems PhD

University of surrey.

Why choose this programme On our Robotics and Autonomous Systems PhD, you’ll study, design and build novel solutions and behaviours for Read more...

• 4 years Full time degree: £4,712 per year (UK)
• 8 years Part time degree: £2,356 per year (UK)

Artificial Intelligence and Intelligent Agents PhD

Bangor university.

Research topics include knowledge-based systems, logic, multi-agent systems, distributed systems, machine learning, data mining, Read more...

Artificial Intelligence Enabled Healthcare MRes and MPhil/PhD

Ucl (university college london).

The CDT programme consists of a 1 year MRes followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the Read more...

• 1 year Full time degree: £6,035 per year (UK)
• 2 years Part time degree: £3,015 per year (UK)

PhD with Integrated Study Machine Intelligence for Nano-electronic Devices (MINDS)

University of southampton.

This four-year iPhD is designed to develop and nurture the next generation of technology pioneers who will have the skills, assets and Read more...

• 4 years Full time degree

PhD Robotics

Sheffield hallam university.

Course summary Undertake extensive, supervised studies in the Centre for Automation and Robotics Research Specialise in pertinent Read more...

• 4 years Full time degree: £4,786 per year (UK)
• 7 years Part time degree: £2,393 per year (UK)

Artificial Intelligence Machine Learning and Advanced Computing PhD

One fully-funded 4-year PhD scholarship is available to start in September 2023 in the area of Artificial Intelligence machine learning Read more...

Computer Science PhD, MPhil - Knowledge Discovery and Machine Learning

University of leicester.

Computing at Leicester offers supervision for the degrees of Doctor of Philosophy (PhD) - full-time and part-time Master of Philosophy Read more...

DPhil in Autonomous Intelligent Machines and Systems (EPSRC Centre for Doctoral Training)

University of oxford.

The Autonomous Intelligent Machines and Systems (AIMS) Centre for Doctoral Training (CDT) provides graduates with the opportunity to Read more...

• 4 years Full time degree: £9,500 per year (UK)
• 8 years Part time degree: £4,750 per year (UK)

Text and Data Mining (PhD/MPhil)

Cardiff university.

Focus your studies on text and data mining through our Computer Science and Informatics research programmes (MPhil, PhD). Studying for a Read more...

• 3 years Full time degree
• 5 years Part time degree

Practice-Oriented Artificial Intelligence PhD

University of bristol.

Practice-oriented artificial intelligence is about bridging the gap between complex problem domains such as those found in science and Read more...

• 4 years Full time degree: £4,758 per year (UK)

Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology PhD

The university of edinburgh.

The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of Read more...

PhD Intelligent Systems

Ulster university.

The vision is to develop a bio-inspired computational basis for Artificial Intelligence to power future cognitive technologies. Our mission Read more...

• 6 years Part time degree: £2,390 per year (UK)

Statistics and Machine Learning (DPhil)

The Modern Statistics and Statistical Machine Learning CDT is a four-year DPhil research programme (or eight years if studying Read more...

Informatics: AIAI: Foundations and Applications of Artificial Intelligence, Automated Reasoning, Agents, Data Intensive Research PhD

At the Artificial Intelligence and its Applications Institute, we enable computer systems to reproduce and complement human abilities, work Read more...

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What's it like to do a PhD in artificial intelligence?

UQ people Published 5 Jan, 2023  ·  4-minute read

Studying a PhD in artificial intelligence (AI) places you at the forefront of cutting-edge technology.

Artificial intelligence research continues to expand and evolve every day as we appreciate what our future could look like. This type of technology can help solve all kinds of problems, from small-scale, everyday complications to larger-than-life challenges.

An artificial intelligence PhD can kickstart your research career in this fascinating field and open many other doors as you explore new ways to problem-solve using technology.

We talked to UQ Postdoctoral Research Fellow Sam Hislop-Lynch about his experience studying a Doctor of Philosophy and what he hopes his research using artificial intelligence, and more specifically computer vision (CV), will achieve.

Learn more about studying a PhD at UQ .

PhD topics in artificial intelligence

PhD research topics in artificial intelligence can vary dramatically depending on your interests. Whether you choose your own PhD topic or join an existing PhD project , you could find yourself pursuing research in the areas of:

• data management and data science
• computer vision and multimedia computation
• machine learning and data mining
• optimisation and statistics
• human-centred AI.

Sam landed on a computer vision PhD topic after working with the technology during his undergrad and postgrad studies. His research focuses on optimising traffic flow.

“Motorways often suffer from flow breakdown around ramps and weaving segments, and with our current technology, it's difficult to investigate this and propose solutions,” he says.

“I developed a suite of algorithms and software to allow researchers and practitioners to fly many drones above a motorway, stitch the imagery together and then extract vehicle trajectories from the footage.”

There are just some things that machines can achieve more efficiently and reliably than humans, and Sam’s research is testament to that.

“Ultimately, the research will reveal strategies that can be used to alleviate or prevent traffic flow breakdowns,” he says.

“This might take the form of better road design guidelines or even real-time strategies for mitigating congestion.”

It’s all about being able to see what’s working and what’s not, and it’s computer vision that allows us to do this.

These findings have a knock-on effect on the environment too. Less traffic congestion and stalling means a reduction in vehicle emissions.

At the end of the day, AI is helping us to better understand how we can use machines efficiently and responsibly, and the research undertaken during a PhD can help us to makes strides in this area.

Is a PhD in artificial intelligence worth it?

So, what are the perks of completing a PhD in AI? According to Sam, it teaches you invaluable skills in problem-solving, which can help open many other doors down the line and expand your professional network.

I began working with AI/CV technology because it allowed me to investigate problems that would otherwise be impossible to solve with human pattern recognition alone.

“AI/CV technology allows me to process large amounts of video data with only very little human input.”

Sam recognised that this type of technology was very valuable to several industries outside of transport engineering. The skills he has developed in artificial intelligence and computer vision can be applied in a range of settings and projects, to great effect.

“I’ve leveraged the knowledge that I obtained during my PhD to begin developing software for the mining industry.”

“I intend to pursue this further and expand the scope and number of products I offer,” he says.

Since the completion of his PhD, Sam has branched out into consultancy work while continuing his research. His experience is living proof that a PhD in AI is worth it, if you make the most of the professional opportunities available and think big when it comes to using the skills you’ve learned.

So, what should you consider when deciding whether a PhD in artificial intelligence is the right path for you? Sam encourages you to ask yourself the following:

• Are you interested in the topic?
• Does it align with your long-term goals?
• Where else can this knowledge be applied?
• Can the skills you will learn be used to solve other problems and leverage a better job?

Why study a PhD in artificial intelligence at UQ?

It isn’t just because we have a beautiful campus, state-of-the-art facilities, strong connections with industry leaders and a focus on professional development opportunities. Though all those things certainly help.

Read more about the benefits of studying your PhD at UQ .

Sam explains the specific benefits of studying an artificial intelligence PhD at UQ.

“Apart from the high academic standards and many knowledgeable staff to draw upon, UQ hosts the Wiener supercomputer,” he says.

“This is a purpose-built, bare-metal cluster full of graphical processing units (GPUs) which is well suited to computer vision and AI based research.”

Furthermore, UQ researchers are making strides in several disciplines with the use of AI , including health and medicine, business, society and government, mining, agriculture, defence and energy. Our people are using AI research to protect children , detect cancer and mitigate cyber-attacks .

You too could study a PhD in artificial intelligence at UQ to create positive and lasting change with your research.

Ready to follow your passion for research with a PhD in AI?

Learn more about studying a PhD at UQ

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PhD in Artificial Intelligence

To enter the Doctor of Philosophy in Artificial Intelligence, you must apply online through the UGA  Graduate School web page . There is an application fee, which must be paid at the time the application is submitted.

There are several items which must be included in the application:

• Standardized test scores, including the GRE. 
• 3 letters of recommendation, preferably from university faculty and/or professional supervisors. We encourage you to submit the letters to the graduate school online as you complete the application process.
• A sample of scholarly writing, in English. This can be anything you've written but should give an accurate indication of your writing abilities. The writing sample can be a term paper, research report, journal article, published paper, college paper, etc.
• A completed  Application for Graduate Assistantship , if you are interested in receiving funding. 
• A Statement of Purpose.
• A Resume or Curriculum Vitae.

Further information on program admissions is found in the AI Institute Frequently Asked Questions (FAQ) . 

International Students should also review the links on the  Information for International Students  page for additional information relevant to the application process.

Graduate School Policies

University of Georgia Graduate School policies and requirements apply in addition to (and, in cases of conflict, take precedence over) those described here. It is essential that graduate students familiarize themselves with Graduate School policies, including minimum and continuous enrollment  and other policies contained in the Graduate School Bulletin.

Students should also familiarize themselves with Graduate School Dates and Deadlines relevant to the degree.

Degree Requirements

Students of the doctoral program must complete a minimum of 40 hours of graduate coursework and 6 hours of dissertation credit (for a total of 46 credit hours), pass a comprehensive examination, and write and defend a dissertation. In addition, the University requires that all first-year graduate students enroll in a 1-credit-hour GradFirst seminar . Each of these requirements is described in greater detail below.

The degree program is offered using an in-person format, and classes are in general scheduled for full-time students. There are currently no special provisions for part-time, online, or off-campus students. Students are expected to attend all meetings of classes for which they are registered.

Program of Study

The Program of Study must include a minimum of 40 hours of graduate course work and a minimum of 6 hours of dissertation credit. Of the 40 hours of graduate course work, at least 20 hours must be 8000-level or 9000-level hours.

Required Courses

The following courses must be completed unless specifically waived for students entering the program with a master’s degree in Artificial Intelligence or a related field, or for students with substantially related graduate course work. All waived credits may be replaced by an equal number of doctoral research or doctoral dissertation credits (ARTI 9000, Doctoral Research or ARTI 9300, Doctoral Dissertation). Substitutions must be approved for a particular student by that student's Advisory Committee and by the Graduate Coordinator.

• PHIL/LING 6510  Deductive Systems (3 hours)
• CSCI 6380  Data Mining (4 hours) or CSCI 8950  Machine Learning (4 hours)
• CSCI/PHIL 6550  Artificial Intelligence (3 hours)
• ARTI 6950  Faculty Research Seminar (1 hour)
• ARTI/PHIL 6340 Ethics and Artificial Intelligence (3 hours)

Elective Courses

In addition to the required courses above, at least 6 additional courses must be taken from Groups A and Group B below, subject to the following requirements. 

• At least 2 courses must be taken from Group A, from at least 2 areas.
• At least 2 courses must be taken from Group B, from at least 2 areas.
• At least 3 courses must be taken from a single area comprising the student’s chosen area of emphasis .

Since not all courses have the same number of credit hours, Ph.D. students may need to take additional graduate courses to complete the 40 hours.

AREA 1: Artificial Intelligence Methodologies

• CSCI 6560  Evolutionary Computing (4 hours)
• CSCI 8050  Knowledge Based Systems (4 hours)
• CSCI/PHIL 8650  Logic and Logic Programming (4 hours)
• CSCI 8920  Decision Making Under Uncertainty (4 hours)
• CSCI/ENGR 8940  Computational Intelligence (4 hours)
• CSCI/ARTI 8950  Machine Learning (4 hours)

AREA 2: Machine Learning and Data Science

• CSCI 6360  Data Science II (4 hours)
• CSCI 8360  Data Science Practicum (4 hours)
• CSCI 8945  Advanced Representation Learning (4 hours)
• CSCI 8955  Advanced Data Analytics (4 hours)
• CSCI 8960  Privacy-Preserving Data Analysis (4 hours)

AREA 3: Machine Vision and Robotics

• CSCI/ARTI 6530  Introduction to Robotics (4 hours)
• CSCI 6800  Human Computer Interaction (4 hours)
• CSCI 6850  Biomedical Image Analysis (4 hours)
• CSCI 8850  Advanced Biomedical Image Analysis (4 hours)
• CSCI 8820  Computer Vision and Pattern Recognition (4 hours)
• CSCI 8530  Advanced Topics in Robotics (4 hours)
• CSCI 8535  Multi Robot Systems (4 hours)

AREA 4: Cognitive Modeling and Logic

• PHIL/LING 6300  Philosophy of Language (3 hours)
• PHIL 6310  Philosophy of Mind (3 hours)
• PHIL/LING 6520  Model Theory (3 hours)
• PHIL 8310  Seminar in Philosophy of Mind (max of 3 hours)
• PHIL 8500  Seminar in Problems of Logic (max of 3 hours)
• PHIL 8600  Seminar in Metaphysics (max of 3 hours)
• PHIL 8610  Epistemology (max of 3 hours)
• PSYC 6100  Cognitive Psychology (3 hours)
• PSYC 8240  Judgment and Decision Making (3 hours)
• CSCI 6860  Computational Neuroscience (4 hours)

AREA 5: Language and Computation

• ENGL 6885  Introduction to Humanities Computing (3 hours)
• LING 6021  Phonetics and Phonology (3 hours)
• LING 6080  Language and Complex Systems (3 hours)
• LING 6570  Natural Language Processing (3 hours)
• LING 8150  Generative Syntax (3 hours)
• LING 8580  Seminar in Computational Linguistics (3 hours)

AREA 6: Artificial Intelligence Applications

• ELEE 6280  Introduction to Robotics Engineering (3 hours)
• ENGL 6826  Style: Language, Genre, Cognition (3 hours)
• ENGL/LING 6885  Introduction to Humanities Computing (3 hours)
• FORS 8450  Advanced Forest Planning and Harvest Scheduling (3 hours)
• INFO 8000  Foundations of Informatics for Research and Practice
• MIST 7770  Business Intelligence (3 hours)

Students may under special circumstances use up to 6 hours from the following list to apply towards the Electives group requirement. 

• ARTI 8800  Directed Readings in Artificial Intelligence
• ARTI 8000  Topics in Artificial Intelligence

Other courses may be substituted for those on the Electives lists, provided the subject matter of the course is sufficiently related to artificial intelligence and consistent with the educational objectives of the Ph.D. degree program. Substitutions can be made only with the permission of the student's Advisory Committee and the Graduate Coordinator.

In addition to the specific PhD program requirements, all first-year UGA graduate students must enroll in a 1 credit-hour GRSC 7001 (GradFIRST) seminar which provides foundational training in research, scholarship, and professional development. Students may enroll in a section offered by any department, but it is recommended that they enroll in a section offered by AI Faculty Fellows for AI students. More information is available at the  Graduate School website .

Core Competency

Core competency must be exhibited by each student and certified by the student’s advisory committee. This takes the form of achievement in the required courses of the curriculum. Students entering the Ph.D. program with a previous graduate degree sufficient to cover this basic knowledge will need to work with their advisory committee to certify their core competency. Students entering the Ph.D. program without sufficient graduate background to certify core competency must take at least three of the required courses, and then pursue certification with their advisory committee. A grade average of at least 3.56 (e.g., A-, A-, B+) must be achieved for three required courses (excluding ARTI 6950). Students below this average may take the fourth required course and achieve a grade average of at least 3.32 (e.g., A-, B+, B+, B).

Core competency is certified by the unanimous approval of the student's Advisory Committee as well as the approval by the Graduate Coordinator. Students are strongly encouraged to meet the core competency requirement within their first three enrolled academic semesters (excluding summer semester).  Core Competency Certification must be completed before approval of the Final Program of Study.

Comprehensive Examination

Each student of the doctoral program must pass a Ph.D. Comprehensive Examination covering the student's advanced coursework. The examination consists of a written part and an oral part. Students have at most two attempts to pass the written part. The oral part may not be attempted unless the written part has been passed.

Admission to Candidacy

The student is responsible for initiating an application for admission to candidacy once all requirements, except the dissertation prospectus and the dissertation, have been completed.

Dissertation and Dissertation Credit Hours

In addition to the coursework and comprehensive examination, every student must conduct research in artificial intelligence under the direction of an advisory committee and report the results of his or her research in a dissertation acceptable to the Graduate School. The dissertation must represent originality in research, independent thinking, scholarly ability, and technical mastery of a field of study. The dissertation must also demonstrate competent style and organization. While working on his/her dissertation, the student must enroll for a minimum of 6 credit hours of ARTI 9300 Doctoral Dissertation spread over at least 2 semesters.

Advisory Committee

Before the end of the third semester, each student admitted into the program should approach relevant faculty members and form an advisory committee. Until the committee is formed, the student will be advised by the graduate coordinator. The committee consists of a major professor and two other faculty members, as follows:

• The major professor and at least one other member must be full members of the Graduate Program Faculty.
• The major professor and at least one other member must be Institute for Artificial Intelligence Faculty Fellows.

Deviations from the 3-member advisory committee structure, including having more members, are in some cases permitted but must conform to Graduate School policies. 

The major professor and advisory committee shall guide the student in planning the dissertation.  The committee shall agree upon, document, and communicate expectations for the dissertation. These expectations may include publication or submission requirements, but, should not exceed reasonable expectations for the given research domain. During the planning stage, the student will prepare a dissertation prospectus in the form of a detailed written dissertation proposal. It should clearly define the problem to be addressed, critique the current state-of-the-art, and explain the contributions to research expected by the dissertation work. When the major professor certifies that the dissertation prospectus is satisfactory, it must be formally considered by the advisory committee in a meeting with the student. This formal consideration may not take the place of the comprehensive oral examination.

Approval of the dissertation prospectus signifies that members of the advisory committee believe that it proposes a satisfactory research study. Approval of the prospectus requires the agreement of the advisory committee with no more than one dissenting vote as evidenced by their signing an appropriate form to be filed with the graduate coordinator’s office.  

Graduation Requirements - Forms and Timeline

Before the end of the third semester in residence, a student must begin submitting to the Graduate School, through the graduate coordinator, the following forms: (i) a Preliminary Program of Study Form and (ii) an Advisory Committee Form. The Program of Study Form indicates how and when degree requirements will be met and must be formulated in consultation with the student's major professor. An Application for Graduation Form must also be submitted directly to the Graduate School. Forms and Timing must be submitted as follows:

• Advisory Committee Form (G130)—end of third semester
• Core Competency Form (Internal to IAI)—beginning of fourth semester
• Preliminary Doctoral Program of Study Form—Fourth semester
• Final Program of Study Form (G138)—before Comprehensive Examination
• Application for Admission to Candidacy (G162)—after Comprehensive Examination
• Application for Graduation Form (on Athena)—beginning of last semester
• Approval Form for Doctoral Dissertation (G164)—last semester
• ETD Submission Approval Form (G129)—last semester

Students should frequently check the Graduate School Dates and Deadlines webpage to ensure that all necessary forms are completed in a timely manner.

Student Handbook

Additional information on degree requirements and AI Institute policies can be found in the AI Student Handbook .

For information regarding the graduate programs in IAI, please contact: 

Evette Dunbar [email protected] Boyd GSRC, Room 516 706-542-0358

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Doctor of Philosophy (PhD) in Artificial Intelligence

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Earn a doctorate degree in Artificial Intelligence, help lead innovation in a growing industry

The PhD in Artificial Intelligence is centered upon how computers operate to match the human decision making process in the brain. Your research will be led by AI experts with both research and industrial expertise. This emerging subject is starting to attract attention on the wider issues as the IOT and other advanced computer systems work in our lives.

This is a research based doctorate PhD degree where you will be assigned an academic supervisor almost immediately to guide you through your program and is based on mostly independent study through the entire program. It typically takes a minimum of two years but typically three years to complete if a student works closely with their assigned academic advisor. Under the guidance of your academic supervisor, you will conduct unique research in your chosen field before submitting a Thesis or being published in three academic journals agreed to by the academic supervisor.  If by publication route it will require original contribution to knowledge or understanding in the field you are investigating.

As your PhD progresses, you move through a series of progression points and review stages by your academic supervisor. This ensures that you are engaged in a process of research that will lead to the production of a high-quality Thesis and/or publications and that you are on track to complete this in the time available. Following submission of your PhD Thesis or accepted three academic journal articles, you have an oral presentation assessed by an external expert in your field.

Why Capitol?

Learn around your busy schedule

Program is 100% online, with no on-campus classes or residencies required, allowing you the flexibility needed to balance your studies and career.

Proven academic excellence

Study at a university that specializes in industry-focused education in technology fields, with a faculty that includes many industrial and academic experts.

Expert guidance in doctoral research

Capitol’s doctoral programs are supervised by faculty with extensive experience in chairing doctoral dissertations and mentoring students as they launch their academic careers. You’ll receive the guidance you need to successfully complete your doctoral research project and build credentials in the field. 

Key Faculty

Vice President

Dissertation Chair

Degree Details

This program may be completed with a minimum of 60 credit hours, but may require additional credit hours, depending on the time required to complete the dissertation/publication research. Students who are not prepared to defend after completion of the 60 credits will be required to enroll in RSC-899, a one-credit, eight-week continuation course. Students are required to be continuously enrolled/registered in the RSC-899 course until they successfully complete their dissertation defense/exegesis.

The student will produce, present, and defend a doctoral dissertation after receiving the required approvals from the student’s Committee and the PhD Review Boards.

Prior Achieved Credits May Be Accepted

Doctor of Philosophy - 60 credits

(Prerequisite: None)	6
(Prerequisite: AIT-800)	6
(Prerequisite: AIT-810)	6
(Prerequisite: AIT-820)	6
(Prerequisite: AIT-830)	6
(Prerequisite: AIT-840)	6
(Prerequisite: AIT-900)	6
(Prerequisite: AIT-910)	6
(Prerequisite: AIT-920)	6
(Prerequisite: AIT-930)	6

Program Objectives:

• Students will integrate and synthesize alternate, divergent, or contradictory perspectives or ideas fully within the field of Artificial Intelligence.
• Students will demonstrate advance knowledge and competencies in Artificial Intelligence.
• Students will analyze existing theories to draw data-supported consultations in Artificial Intelligence.
• Students will analyze theories, tools, and frameworks used in Artificial Intelligence.
• Students will execute a plan to complete a significant piece of scholarly work in Artificial Intelligence.
• Students will evaluate the legal, social, economic, environmental, and ethical impact of actions within Artificial Intelligence and demonstrate advance skill in integrating the results in to the leadership decision-making process.

Learning Outcomes:

Upon graduation, graduates will:

• integrate the theoretical basis and practical applications on Artificial Intelligence in to their professional work;
• demonstrate the highest mastery of Artificial Intelligence;
• evaluate complex problems, synthesize divergent/alternative/contradictory perspectives and ideas fully, and develop advanced solutions to Artificial Intelligence challenges; and
• contribute to the body of knowledge in the study of Artificial Intelligence.

Tuition & Fees

Tuition rates are subject to change.

The following rates are in effect for the 2024-2025 academic year, beginning in Fall 2024 and continuing through Summer 2025:

• The application fee is $100
• The per-credit charge for doctorate courses is $950. This is the same for in-state and out-of-state students.
• Retired military receive a $50 per credit hour tuition discount
• Active duty military receive a $100 per credit hour tuition discount for doctorate level coursework.
• Information technology fee $40 per credit hour.
• High School and Community College full-time faculty and full-time staff receive a 20% discount on tuition for doctoral programs.

Find additional information for 2024-2025 doctorate tuition and fees.

When I was comparing multiple universities, Capitol Tech's admissions staff was responsive, helpful, and caring. This was valuable my deciding factor in choosing Capitol Tech.

-Dr. Jason Collins-Baker PhD in Artificial Intelligence

I chose Capitol for its great reputation in the field I am interested in, as well as its flexibility offering a fully Online program that works very well with my work and family commitments. Another reason was its European PhD program, with which I am familiar since I grew up and studied in Europe (Spain), although I currently live in the United States (California).

-Hector Garcia Villa PhD in Artificial Intelligence

Need more info, or ready to apply?

PhD Admissions

Phd admissions july 2024.

Date	Announcement
July 8th, 2024	We have completed the PHD selection process. The offer letters have been mailed to all the selected candidates.
May 29th, 2024	Please note that the shortlisting process for PhD programs has been completed, and the shortlisted candidates have been informed through email. We advise all applicants to check their email thoroughly (including the spam folder). You can find the application numbers of the shortlisted candidates here: &
May 14th, 2024	PhD Admission process will be starting on tentatively in physical mode.
April 14th, 2024	Application deadline has been extended until
March 20th, 2024	PhD admissions portal opens for July 2024 round of admissions. Click to apply. The PhD admissions brochure for the AI department can be found . The Tentative Deadline for Online Application is April 11th, 2024. Keep visiting this page for all Latest Updates.

PhD Admissions January 2024

Date	Announcement
December 14, 2023	Candidates selected for the PhD program are informed by email on December 6th, 2023. This concludes our PhD admissions for January 2024 round. We sincerely thank and send our best wishes to all candidates who expressed their interest in the program.
November 09, 2023	Candidates shortlisted for the written test and interview to PhD admission will be informed by Nov 10. Shortlisted candidates have to give a written test and interview physically on campus on Nov 23rd and Nov 24th respectively.
October 12, 2023	PhD admissions portal opens for January 2024 round of admissions. Click to apply. Last date for applications is 26th-October-2023. The PhD admissions brochure for the AI department can be found .

May 20, 2023: Candidates shortlisted for written test has been informed though their regsitered email. Please check your spam folder also.

May 09, 2023: Candidates shortlisted based on the written test performance will be called for interviews at IITH campus. The interviews will be held on the 9th June 2023. Requests for online interviews will not be considered.

May 09, 2023: The online written test for the PhD program will be held on 27th May 2023, from 10 am. Details about the modalities of the test will be informed to the shortlisted candidates by email. Shortlisting is currently under progress, and is expected to be completed by early next week.

PhD Admissions July 2023

Date	Information
July 4, 2023	PhD offer letters were sent to the selected candidates on 19th June, 2023
May 31, 2023	Interview call letters haven been sent to candidates shortlisted from written test. The interview date, time, as well as the reporting instructions are mentioned in the email.
May 20, 2023	Candidates shortlisted for written test has been informed though their registered email. Please check your spam folder also.
May 09, 2023	Candidates shortlisted based on the written test performance will be called for interviews at IITH campus. The interviews will be held on the 9th June 2023. Requests for online interviews will not be considered.
May 09, 2023	The online written test for the PhD program will be held on 27th May 2023, from 10 am. Details about the modalities of the test will be informed to the shortlisted candidates by email. Shortlisting is currently under progress, and is expected to be completed by early next week.
March 27, 2023	The Department of AI is currently accepting applications for PhD admissions for the July 2023 cycle. The link to the PhD admission portal can be found . The brochure containing the instructions can be downloaded from . The deadline is .

The Department of AI at IIT Hyderabad helps in creating a complete ecosystem for AI Academics and Research at IIT Hyderabad. The department offers B.Tech, M.Tech. and PhD programs (full time and part time). The emergence of the department is the consequence of the heavy demand from the industry on artificial intelligence and data analytics, and the need to tackle the many fundamental and foundational research problems arising in a rapidly evolving field. The department's mission is to

• Train students to have a sound understanding of the fundamentals of the theory and practise of Artificial Intelligence and Machine Learning.
• Enable students to become leaders in the industry and academia nationally and internationally
• Meet the pressing demands of the nation in the areas of Artificial Intelligence and Machine Learning.

The Department of Artificial Intelligence has a sizable number of highly dedicated and dynamic faculty members, with a wide variety of expertise. Faculty publishes regularly in top AI conferences and journals such as NeurIPS, ICML, AAAI, IJCAI, CVPR, ICCV, ACL, EMNLP, AAMAS, JMLR, TIP, TACL etc. Currently there are 29 faculty members affiliated with the AI department. Candidates can discover the profiles and teaching/research areas of different faculty members by visiting Faculty Profiles

The PhD students will be provided with excellent laboratory facilities that they can access at any time. The laboratories are equipped with state-of-the-art facilities including software and hardware (including high-end DGX servers and other GPU servers).

The Department of AI seeks talented and dedicated PhD students to develop highly skilled human resources who can contribute to modern and cutting edge research in AI.

Eligibility Criteria for Application to the PhD program:

The Eligibility criteria for regular PhD program is MTech/Equivalent Degree in CS/ECE/EE or related areas in Artificial Intelligence. The applicant must have already earned the Master's degree, or should at least be in the final year of the respective pro-gram. In the latter case, it would be assumed that the candidate will be able to complete the degree by the time of admission into the PhD program. Candidates with MSc/MCA/equivalent PG degree with an EMDS at IIT-H may apply for Regular PhD program.

The Department of AI considers Direct-PhD admission , where the eligibility criteria is BTech/BE/MCA/MSc/Equivalent Degree in any engineering discipline or Maths, and have a valid GATE score in CS/EC/EE/MA/ST. Candidate with BTech/BE/MCA/MSc/Equivalent Degree in any engineering discipline or Maths from any Centrally Funded Technological Institutes (CFTI), with CGPA 9 or above can apply without a valid GATE score (For candidates from IITs/NITs/IIITs this is relaxed to CGPA 8 or above).

The Department of AI also considers Direct-Phd admission , where the eligibility criteria is BTech/BE/MCA/MSc/Equivalent Degree in any engineering discipline or Maths, and have a valid GATE score in CS/EC/EE/MA/ST. Candidate with B.Tech/M.Sc in any engineering discipline or Maths from any Indian Institute of Technology (IIT), with CGPA 8 or above can apply without a valid GATE score

Department also admit students to the part time PhD program . Part time PhD program have the same eligibility criteria listed above and in addition the candidate must be employed with a relevant employer at the time of application and admission.

--> --> --> -->

Type	Eligibility
Regular PHD Program
Part Time PHD Program
Direct PHD Program

For admission to the program, there will be a preliminary shortlisting depending upon the academic background. Candidates who satisfy the shortlisting criteria need to appear for a written test and/or an interview. The shortlisting criteria could be higher than the eligibility criteria. In addition, the department also reserves the right to set any cut off criteria for shortlisting the candidates and not to select anybody if no appropriate candidates are found.

IIT Hyderabad has a centralized online application portal for the PhD admission. Candidates are requested to visit IITH PHD Admissions for the application format and the submission link. Candidates applying for any of the PhD program are requested to submit their application through the portal. For other related information, such as last date of application, shortlist announcement, written test and interview dates, please visit https://ai.iith.ac.in/phd-admissions.html

For any query, kindly write to​ ​[email protected]

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Best Online Doctorates in Artificial Intelligence: Top PhD Programs, Career Paths, and Salary

The best online PhD in Artificial Intelligence (AI) can get you a high-paying job at a tech company or an exciting position in a research institution. You don’t have to go to college on-campus to earn an online PhD in Artificial Intelligence. All you need is a laptop and a stable Internet connection.

Getting an artificial intelligence PhD online opens the door to many career options and improves your qualifications in the job market. Artificial intelligence jobs are well-paid and will continue to grow in importance in the years to come. Continue reading to learn more about the best PhD programs to help you narrow your school search and begin your doctoral studies.

Find your bootcamp match

Can you get a phd in artificial intelligence online.

Yes, you can get a PhD in Artificial Intelligence online. However, there aren’t many educational programs offering this online degree since AI is most commonly offered as a specialization area in doctoral programs in computer and data science. Additionally, many universities offer PhD degrees only on campus.

There are many online graduate degrees in machine learning, a subset of artificial intelligence. Additionally, there are several doctoral degrees available in big data, a fundamental tool for applied artificial intelligence technologies. You may want to consider including PhD Degrees in Data Science in your school search, as they offer well-structured curricula geared toward AI.

Is an Online PhD Respected?

Yes, an online PhD is respected. It’s a doctoral degree with academic equivalence to in-campus programs. Degree holders can access the same career opportunities and research positions in AI as on-campus graduates.

The respect from employers and research institutions for online PhD programs is mainly related to the reputation of the school, regardless of whether it’s an on-campus or online degree. Be sure to apply only to online PhD degrees from schools with official accreditation and that host relevant artificial intelligence research projects.

What Is the Best Online PhD Program in Artificial Intelligence?

The best online PhD program in artificial intelligence is the Doctor of Science in Computer Science from Aspen University. This entirely online doctoral program offers an excellent education in AI and is flexible and affordable for busy professionals and part-time students.

Why Aspen University Has the Best Online PhD Program in Artificial Intelligence

Aspen University has the best PhD program in artificial intelligence because it allows students to focus on their passion by choosing a capstone project of their interest within the branch of AI. With a total cost of $30,500, the Doctor of Science in Computer Science is the most affordable online doctoral program.

Students enrolled at Aspen University have up to ten years to complete the program at their own pace. Graduates from this program can join research teams in the public or private sectors to find and develop new applications for artificial intelligence and software engineering.

Best Online Master’s Degrees

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Online PhD in Artificial Intelligence Admission Requirements

The admission requirements for an online PhD in Artificial Intelligence vary considerably depending on the institution. For example, Capitol Technology University requires a master’s degree in a relevant field, two letters of recommendation, and a resume that shows five years of related work experience.

On the other hand, the PhD in Computer Science from Northcentral University only requires a master’s degree from an accredited institution. The online Doctor of Science in Computer Science from Aspen University requires applicants to be competent object oriented programming (OOP) developers, or they can take prerequisite courses to fulfill the requirement.

Some universities require GRE exam scores and personal statement essays, and all programs will require official transcripts of previous academic work. Prospective English as a second language (ESL) and international students will need to provide proof of English proficiency in the form of the Test of English as a Foreign Language (TOEFL) exam scores.

• Master’s degree in fields relevant to AI, computer science, or machine learning
• Resume showing relevant work experience
• GRE exam scores, depending on the school
• TOEFL exam or equivalent for ESL and international students
• Letters of recommendation
• Entrance essay or personal statement describing your research interests
• Interview by phone or in-person with a representative of the doctoral program, depending on the school

Best Online PhDs in Artificial Intelligence: Top Degree Program Details

School	Program	Estimated Length
Aspen University	Doctor of Science in Computer Science	2 to 10 years
Capitol Technology University	PhD in Artificial Intelligence	3 years
Colorado Technical University	Doctorate in Big Data Analytics Online	3 years
Northcentral University	PhD in Computer Science	4 years
University of North Dakota	PhD in Computer Science	4 to 5 years

Best Online PhDs in Artificial Intelligence: Top University Programs to Get a PhD in Artificial Intelligence Online

The top university programs to get a PhD in Artificial Intelligence online include Aspen University, Capitol Technology University, Colorado Technical University, Northcentral University, and the University of North Dakota. 

These schools offer the best online AI PhDs, which include PhDs in Artificial Intelligence and Big Data Analysis doctoral programs. Some online educational programs in computer science offer specialization courses in artificial intelligence and machine learning as part of their curricula.

Aspen University is a modern private institution with broad academic programs for distance learning. It has four schools offering online bachelor’s degrees and master’s and doctoral programs. Aspen has an open admission policy, and courses begin every two weeks. The university provides a self-paced approach to studying, and students have between two and ten years to complete any graduate degree.

Doctor of Science in Computer Science

The Doctor of Science in Computer Science program from the School of Business and Technology at Aspen University includes courses in artificial intelligence, the automata complexity theory, and computer ethics. Graduate students of this program will also have the opportunity to work on a research dissertation of their interest. 

Doctor of Science in Computer Science Overview

• Accreditation: Distance Education Accrediting Commission
• Program Length: 2 to 10 years (self-paced)
• Acceptance Rate: 100% (open admission policy)
• Tuition and Fees: $30,500

Doctor of Science in Computer Science Admission Requirements

• Demonstrate professional object oriented programming (OOP) skills
• Master’s degree 
• Official transcripts with a GPA of 3.0 or higher
• Statement of goals, indicating academic, professional, and personal goals

Capitol Technology University is a private university that was originally founded in 1927 to educate radio and electronics technicians. The university is home to the Space Operation Institute, which partners with NASA to train astronautical engineers. The university has just over 740 students in undergraduate and graduate STEM programs.

PhD in Artificial Intelligence

The PhD in Artificial Intelligence degree program aims to engineer computer systems that match the human brain’s decision-making processes. Students conduct unique research with the guidance of skilled academic supervisors. Students can graduate with a thesis or by publishing three articles in high-impact journals. Sixty credit hours are required to earn the degree.

PhD in Artificial Intelligence Overview

• Accreditation: Middle States Commission on Higher Education
• Program Length: 3 years
• Acceptance Rate: N/A
• Tuition: $933/credit

PhD in Artificial Intelligence Admission Requirements

• A master’s degree in a field relevant to artificial intelligence
• Official transcripts
• Online application and non-refundable application fee of $100
• A resume that demonstrates 5 years of related work experience
• Two recommendation forms 
• Personal essay on your technological expertise and how you are prepared to succeed in the program

Colorado Technical University (CTU) is a private university founded in 1965 to help veterans transition into civilian life. CTU is tech-oriented and offers over 80 undergraduate and graduate programs. The university prides itself on enabling US military personnel stationed overseas to take part in CTU’s numerous online learning options.

Doctorate in Big Data Analytics Online

This comprehensive program focuses on the fundamental theoretical, research, and field applications of AI. You will need 100 credit hours to complete this doctoral program , four of which correspond to participation in two in-campus symposia. The cost of the entire program is $64,640.

Doctorate in Big Data Analytics Online Overview

• Accreditation: Higher Learning Commission
• Tuition and Fees: $64,640 total

Doctorate in Big Data Analytics Online Admission Requirements

• Conversation with an admissions advisor to discuss your academic goals
• Interview by phone or in-person with a department representative
• Entrance essay
• Accredited bachelor’s degree

Northcentral University is an accredited, private online school founded in 1996 and based in San Diego, California. All of NCU’s programs are offered online, and the school has over 10,000 online students. The school does not require GRE, GMAT, or any standardized test scores for admission, and the doctoral faculty offers one-to-one career counseling.

PhD in Computer Science 

You’ll need 60 credit hours to complete the PhD in Computer Science program online at NCU. The total estimated cost of the program is $68,560 and can be completed in 3 years. The curriculum is designed for graduate students to explore theoretical concepts and gain relevant skills in their preferred areas of expertise, including AI, data mining , and cyber security. 

PhD in Computer Science Overview

• Tuition and Fees: $68,560 total or $3,282/credit

PhD in Computer Science Admission Requirements

• Master’s degree from a regionally or nationally accredited academic institution
• Online application

The University of North Dakota (UND) is a public research university that was founded in 1883 and enrolls over 13,000 graduate and undergraduate students. UND is a leading school in aviation, space, and unmanned aircraft education and offers more than 140 graduate degree programs. 

Students enrolled in the PhD in Computer Science program can take specialization courses in AI and machine learning. The comprehensive program requires 90 credits and explores topics such as bioinformatics, compiler design, software engineering , and the practical and theoretical frameworks of computer science. 

• Program Length: 4 to 5 years
• Tuition and Fees: $869/credit
• Master's Degree in Science or Engineering with an overall minimum GPA of 3.0
• Expertise in high-level programming languages and basic knowledge of data structures
• Basic skills in calculus, statistics, and linear algebra
• Online application and non-refundable application fee of $35
• Three letters of recommendation
• Statement of goals

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Online Artificial Intelligence PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Artificial Intelligence?

It can be quite hard to complete an online PhD program in artificial intelligence. According to a study conducted by researchers from Nova Southeastern University, doctoral programs have average attrition rates of over 50 percent and up to 70 percent for online doctoral programs . PhD coursework can be demanding and highly time-consuming for nearly all programs.

Working closely with your academic advisor increases your chances of success and prevents disengagement, especially in online degree programs. An academic advisor will offer career counseling to help you tackle defeatist attitudes. To ensure completion, you should stay in communication with your advisor to make a plan with achievable milestones.

How Long Does It Take to Get a PhD in Artificial Intelligence Online?

It takes about three to five years to get a PhD in Artificial Intelligence online. Some programs take more than five years to complete the credit requirements and the doctoral thesis. Most online programs offer a flexible timeline to complete the degree requirements and are designed for people who are holding a job while they study.

Most programs offer one-to-one career counseling from the doctoral faculty to find solutions for students to allocate time and design strategies to complete the program. Students can pause their studies for set periods of time and resume their studies at a more convenient time for them.

How Hard Is an Online Doctorate in Artificial Intelligence?

An online doctorate in artificial intelligence can be quite hard. It requires good learning skills to complete three to four years of coursework. There is also a considerable financial commitment and long hours of doctoral-level readings and thesis work.

Not all the online PhD programs in artificial intelligence require a high-level education in computer science theory, math, or engineering. Some programs welcome students from any field because nearly every industry is finding new practical applications for AI.

Best PhD Programs

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What Courses Are in an Online Artificial Intelligence PhD Program?

The common courses in an online artificial intelligence PhD program include artificial intelligence research backgrounds, artificial intelligence research methodologies, and artificial intelligence future trends . Some programs also include machine learning, deep learning, and computer vision courses.

Main Areas of Study in an Artificial Intelligence PhD Program

• Machine learning
• Deep learning
• Machine vision
• Natural language processing

How Much Does Getting an Online Artificial Intelligence PhD Cost?

It costs $19,314 on average to get a PhD in Artificial Intelligence, according to the National Center for Education Statistics. The exact costs of the program vary according to the institution. However, this is an average figure, and tuition costs will vary significantly.

Some schools, like the University of North Dakota, allow you to transfer up to 30 credits from approved master’s degree programs. Private schools are typically more expensive than public universities, and some schools will charge more for out-of-state tuition. There are many private and public grants available for students in STEM doctoral programs.

How to Pay for an Online PhD Program in Artificial Intelligence

You can pay for an online PhD in Artificial Intelligence by getting scholarships, grants, loans, and employment or internship opportunities. The University of North Dakota offers positions with remote capabilities that don’t require an on-campus presence. You can also apply for a Direct PLUS Loan from the US Department of Education.

The Federal Student Aid office also offers several types of financial aid for students in graduate programs. Use the Federal Student Aid Estimator to learn how much financial aid you can get from the federal government.

How to Get an Online PhD for Free

You can’t get an online PhD for free. Some online colleges, like Northcentral University, offer a few full-tuition scholarships every semester for online master’s degrees and doctoral programs. The US military also offers full and partial scholarships for service members, their spouses, and children. It’s best to research the financial support options offered by the school.

What Is the Most Affordable Online PhD in Artificial Intelligence Degree Program?

The most affordable online PhD in Artificial Intelligence degree program is the Doctor of Science in Computer Science from Aspen University. The total cost for this doctoral degree is $30,500, consisting of $27,000 in tuition and $3,500 in fees.

Most Affordable Online PhD Programs in Artificial Intelligence: In Brief

School	Program	Tuition
Aspen University	Doctor of Science in Computer Science	$27,000
Capitol Technology University	PhD in Artificial Intelligence	$933 per credit for 60 credits
Colorado Technical University	Doctorate in Big Data Analytics Online	$598 per credit for 100 credits
Northcentral University	PhD in Computer Science with directed research course in Artificial Intelligence	$1,094 per credit for 60 credits
University of North Dakota	PhD in Computer Science with courses in Applications In AI and Machine Learning	$798 per credit for 90 credits

Why You Should Get an Online PhD in Artificial Intelligence

You should get an online PhD in Artificial Intelligence because it is a great way to access better-paid positions and improve your relevance in the job market. With a PhD in AI, you can join skilled teams at companies that are developing new AI applications for every industry or choose to teach and do research in academia.

Top Reasons for Getting a PhD in Artificial Intelligence

• Job opportunities. With a PhD in Artificial Intelligence, you have greater opportunities in the job market. Many companies are creating new positions to integrate AI into their products and services. The demand for specialists in artificial intelligence is increasing, particularly for machine learning analysts and programmers.
• Higher salaries. Employees with expertise in AI are very valuable for employers, regardless of the type of industry. A doctoral degree in artificial intelligence shows that you have the highest level of education in AI and are qualified for positions that pay higher salaries.
• Research opportunities. A doctoral degree in artificial intelligence opens doors to postdoctoral programs and internships in the best AI research centers in the US. There is an increasing demand for AI researchers in public and private institutions, and the research skills gained in a PhD program will highly qualify you for these roles.
• New skills. During your academic journey in a doctoral program in artificial intelligence, you will develop technical skills that will be essential in the emerging AI workforce. These skills include the management of large data sets, natural language processing , and machine learning to improve process precision.

Best Master’s Degree Programs

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What Is the Difference Between an On-Campus Artificial Intelligence PhD and an Online PhD in Artificial Intelligence?

The difference between an on-campus artificial intelligence PhD and an online PhD in Artificial Intelligence is how the schools deliver the courses and the way you interact with faculty. The cost of online PhD programs in artificial intelligence is usually the same as on-campus programs. Online students don’t need to commute or pay campus-related fees.

On-campus programs enforce in-person learning, and students work with faculty and other students on campus to do relevant coursework and research. In an online program, all the relevant learning materials, like digital books, research articles, interactive activities, virtual labs, and discussion forums, are located on the school’s online learning platform.

Online PhD vs On-Campus PhD: Key Differences

• Convenience. With an online PhD program, you don’t need to waste time commuting. You can study from the comfort of your home at the time of your convenience. All the materials, including digital books, lectures, and readings, are located on the online learning platform.
• Affordability. Tuition costs are typically the same for both online and on-campus programs. However, with an online PhD program, you don’t have to pay on-campus related fees, like access to athletic facilities or parking.
• Flexible timelines. Most online PhD programs are designed for students who also hold jobs. Faculty and academic advisors will help you create a program timeline that doesn’t overwhelm you. Additionally, online PhD programs allow you to pause your studies and resume them according to your time availability.
• Location. All you need to complete an online PhD program is a computer and an Internet connection. You can bring your laptop to your workplace or complete your assignments while traveling the world. Many students of online PhD programs are military personnel stationed overseas.

How to Get a PhD in Artificial Intelligence Online: A Step-by-Step Guide

To get a PhD in Artificial Intelligence online, you’ll need to meet the degree requirements of your doctoral program. Your academic advisor can provide career counseling to create a strategy according to your time availability. You will earn your PhD degree after completing all the required credit hours and defending your dissertation.

The first step to obtaining your PhD degree is to prepare your documentation for the application process. This includes official transcripts, a resume, and the contact information of your references. Let your references know the admissions office will write or call them to validate their recommendation and prepare yourself for an interview with the program’s coordinator.

The program faculty will assign you an academic advisor who will provide you with academic counseling during your learning journey. You should work closely with them to set realistic and achievable benchmarks for your coursework. 

Your academic advisor will let you know when it's time to choose an area of specialization. You will then select the elective courses according to that specialization. Choose a specialization area according to your job interests after graduation. 

Most online PhD programs in artificial intelligence require you to complete between 60 and 100 credits. Some of the final credits correspond to your dissertation research. You will have to complete the coursework and write a doctoral dissertation with an original contribution.

The last step to earning your PhD in Artificial Intelligence is to present your final oral examination or dissertation defense. If your research represents an original contribution, and the school approves your publishable dissertation, the PhD degree will be yours.

Online PhD in Artificial Intelligence Salary and Job Outlook

The salary and job outlook for professionals with a doctoral degree in artificial intelligence are higher than average, with most salaries above $100,000 per year. There are several well-paid jobs in industries that are increasing the number of AI projects to develop new products and services.

What Can You Do With an Online Doctorate in Artificial Intelligence? 

With an online doctorate in artificial intelligence, you can get a job in AI , a rapidly growing field with new applications in nearly all industries. Tech and software companies include AI components in their programs and online services to gain a competitive advantage over traditional services.

There are many ways to learn artificial intelligence , but a doctoral degree makes you an expert in AI. With an online doctorate in artificial intelligence, you can apply for internships in AI-leading companies and participate in their algorithm development.

Best Jobs with a PhD in Artificial Intelligence

• Artificial Intelligence Specialist
• Machine Learning Engineer
• Research Engineer
• Technical Architect
• Data Scientist

Potential Careers With an Artificial Intelligence Degree

[query_class_embed] how-to-become-a-*profession

What Is the Average Salary for an Online PhD Holder in Artificial Intelligence?

The average salary for someone with a PhD in Artificial Intelligence is $115,000 per year , according to PayScale. A machine learning engineer has an average annual salary of $112,709 , while a lecturer will earn an average yearly salary of $77,910 . Continue reading below for some of the top-paying jobs available in the field after graduation.

Highest-Paying Artificial Intelligence Jobs for PhD Grads

Online Artificial Intelligence PhD Jobs	Average Salary
Artificial Intelligence Specialist
Machine Learning Engineer
Technical Architect
Data Scientist
Software Developer

Best Artificial Intelligence Jobs for Online PhD Holders

The best artificial intelligence jobs for PhD holders include AI specialist and machine learning engineer. Those that work in this field usually deploy AI models to improve the accuracy and performance of information processes. These jobs require the use of deep learning, machine learning, and natural language processing methods.

Artificial intelligence specialists apply AI principles to find solutions to real-world problems that traditional computing approaches can’t solve. They work in interdisciplinary teams to apply AI components that improve the overall system performance. 

• Salary with an artificial intelligence PhD: $132,995
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, and Washington

Machine learning engineers create and improve engines for processing vast amounts of data, also known as big data. By improving how machine learning models integrate additional sets of data, they increase the predictive capacities of an engine, for example.

• Salary with an artificial intelligence PhD: $124,800

Technical architects with AI expertise design computer networks that follow an AI-centered architecture. They incorporate cloud services into corporate networks, thereby increasing security and reducing costs associated with on-premises computing systems. They also design migration strategies to AI-based computer networks.

• Salary with an artificial intelligence PhD: $118,808
• Job Outlook: 5% job growth from 2020 to 2030
• Number of Jobs: 165,200
• Highest-Paying States: New Jersey, Rhode Island, Delaware, Virginia, and Maryland

Data scientists develop models for data management and processing. They use deep learning techniques to understand customers better and improve products and services with that insight. Algorithm development is one of the most important daily tasks of data scientists.

• Salary with an artificial intelligence PhD: $111,250
• Number of Jobs: 63,200
• Highest-Paying States: Washington, California, Delaware, New York, and New Jersey

Software developers implement artificial intelligent components into programs and online services. These AI components require software development for natural language processing, which is used for voice recognition. Software developers also code programs that use big data to provide accurate search results.

• Salary with an artificial intelligence PhD: $110,140
• Number of Jobs: 1,847,900
• Highest-Paying States: California, Washington, Maryland, New York, and Rhode Island

Is It Worth It to Do a PhD in Artificial Intelligence Online?

Yes, it is worth it to pursue a PhD in Artificial Intelligence online. While a doctoral degree program entails an important investment of time and money, the outcome is worth the effort. A PhD substantially improves your chances of increasing your income with a broader career potential.

Artificial intelligence is a fast-growing field for which all industries are finding new applications every day. An online PhD in Artificial Intelligence shows that you are an expert in AI and that you are qualified to apply for jobs that use artificial intelligence .

Additional Reading About Artificial Intelligence

[query_class_embed] https://careerkarma.com/blog/artificial-intelligence/ https://careerkarma.com/blog/best-artificial-intelligence-bachelors-degrees/ https://careerkarma.com/blog/best-artificial-intelligence-masters-degrees/

Online PhD in Artificial Intelligence FAQ

Anyone who wants to start a career in AI or machine learning can study artificial intelligence. Jobs for artificial intelligence specialists are well-paid and in high demand. You can earn an online degree in artificial intelligence or take an artificial intelligence bootcamp .

Today, artificial intelligence is used in most software-as-a-service (SAAS) applications. It is used in systems that create predictions using big data. AI applications are also found in voice recognition applications, navigation systems, and chatbot assistants.

Artificial intelligence matters because it is increasingly used to improve processes across all domains. Scientists use AI to find new drugs and create new materials. Companies use deep learning to understand their customers better and provide more accurate solutions.

Yes, artificial intelligence will create new jobs. Artificial intelligence engines are increasing their number of cognitive functions to solve problems faster than humans. Meanwhile, there is a growing demand for specialists to find new applications for AI.

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Can I get a PhD in artificial intelligence with a pure mathematics background?

I am currently finishing my master in pure mathematics. I've studied mainly logic, mathematical analysis a bit of probability and I'm writing my thesis about Riemannian Geometry. I've been thinking about doing a PhD, but I am not so sure if I really want (and if I am good enough) to stay in the Academia for all my life.

Even if I don't know anything about, I find the Artificial Intelligence field really fascinating but I know that my current background is inadequate.

Do you think I could get I PhD in that area (possibly using also my mathematical background)?

And what kind of career path could I follow after? I mean, is it easy to find a job related to AI?

• mathematics
• changing-fields

• 1 AI can be used also in physics see here an approach in PRL where designing of new experiments would benefit of "non-human mind". –  Nikey Mike Commented Apr 1, 2016 at 21:01
• Sounds really interesting and fascinating! –  Onil90 Commented Apr 1, 2016 at 21:07
• 2 I edited the post title to reflect the actual question you asked in the body. –  ff524 Commented Apr 1, 2016 at 21:15
• What's you're current status? Are you now able to answer your own question? I think it would be an excellent contribution –  user2768 Commented Oct 24, 2018 at 11:29
• 2 @user2768 I ended up pursuing a PhD in geometric analysis and I didn't apply for any AI PhD position so I guess I can't bring any useful information :/ –  Onil90 Commented Nov 9, 2018 at 8:34

4 Answers 4

I just want to supplement other answers (I have a PhD in AI). If your background is mathematics, I do not agree with others that you need to code per-se. Aside from machine learning pointed above, there are some other sides you can look at that do not require - much - programming (if any).

Logic - Symbolic AI is broadly focused around temporal logics, including those specifically formalised for game-theory, and non-monotonic logics. You can go in many directions with logic in AI: philosophical (i.e., thinking of how to capture certain types of common-sense reasoning, normative reasoning, etc.) or more solution-oriented (i.e., we need a logic to represent a specific problem X and then we will prove some general results about that logic). In the case that you work on logics to solve specific problems, you may need some Computer Science understanding (in particular, proving complexity for this kind of research is required for publishing in many top venues). In terms of the more philosophical aspects, you don't really need so much of a Comp. Sci. background, non-monotonic logics, for example, can be generalised in a graph-theoretical framework.

Game theory - similar, to the problem-oriented logic research I discussed previously, many papers in AI capture some new game-theoretical concepts and aim to prove something. In this area are related topics, such as negotiation (and to some extent, argumentation, although that is more related with non-monotonic logic). For some game-theoretical papers that relax assumptions of heterogeneity in the participants involved in the game (for example), simulation is used to find support for properties that cannot feasibly be proven (often, properties with assumptions of homogonous participants are proven and simulation supplements these results with experiments on trickier cases).

In short, I see no problem with you moving to AI, we have plenty of mathematicians (and philosophers and computer scientists) in the field. In terms of logic or game-theory, you may want to pick the topic carefully if you want to avoid spending time learning 'theoretical' Comp. Sci and/or programming.

• 1 Thank you very much for your answer! :) In the end I decided to keep studying math and at the moment I started a PhD in pure math (geometric analysis). Maybe soon or later I will finally ended up working on AI. I know that there are some applications of differential geometry in AI. –  Onil90 Commented Jan 7, 2017 at 16:45

You have some of the elements of an artificial intelligence major. Your background in logic is pertinent. And your background in mathematical analysis and Riemannian geometry is at least interesting.

What's troubling about what I've seen of your background is your lack of computer science courses. Maybe that's not an issue, if you have the equivalent of a minor in computer science that wasn't featured in your question. Certainly, that would be an important "tool." And if you don't have it, you might consider going back to school for a five or six course minor or certificate program in CS.

The most interesting part of your question was, "I find the Artificial Intelligence field really fascinating." Why is that? And how does that connect with the rest of your background? Or are you starting from scratch, which would be difficult, though not impossible. In any event, these issues should be addressed in your statement of purpose.

Here's my experience in unfamiliar territory: In high school, I had four years of French, and none of Spanish (unless you count my one year of self-taught Spanish). In college, I bluffed my way into a second year Spanish class, and completed it successfully. Here's an example where my interest and natural aptitude compensated for my lack of formal training. Perhaps it will be the same for you and artificial intelligence.

The short answer: definitely yes, if you pick the right topic.

The longer answer: assuming you can catch up with some core programming based on Matlab or numpy/scipy, with your background you should have very good chances in theoretically oriented Machine Learning groups. Riemannian geometry will put you in a very good position for instance to pursue Information Geometry, which is a branch of information theory/probability theory of high relevance to learning theory which makes heavy use of differential geometry.

I know of a student with with a pure math background who continued to do a PhD in AI, with great success and strong publications. He was a good programmer, though, without actually having studied CS formally.

The answer to your question is yes, if you find a proper PhD programe, or better yet a supervisor who is looking for such a student.

Taking into account that Austrian Science Funds an unique PhD program which focuses on using logic in all the areas of CS, perhaps you can find yourself a match there.

The Doctoral college Logical Methods in Computer Science - LogiCS, is currently hiring: http://logic-cs.at/phd/

You must log in to answer this question.

Not the answer you're looking for browse other questions tagged phd mathematics changing-fields ..

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Artificial Intelligence

For students interested in research, the MSc degree provides the opportunity to start a PhD research project in any of the related topics at a university in the Netherlands or abroad. Such positions are regularly available within Utrecht University. There are ample contacts with other universities as well, partly through the nationwide Dutch research school SIKS (School for Knowledge and Information Systems). Some positions are sponsored by companies or the Dutch research council. Graduates may also find challenging positions in R&D departments of international companies, governments, and the banking and insurance sectors.

Doing a PhD

If you have completed your Master's programme, and you are enthusiastic about doing research in your field, then maybe doing a Doctorate (PhD) will interest you. A Doctor’s degree is the highest academic degree awarded by a Dutch university. You start as 'assistant in training' (aio) or 'researcher in training' (oio).

At Utrecht University

At Utrecht University you take part in education in one of the Graduate Schools and often also teach students. During the four-year PhD programme you work under the guidance of a professor on creating a research project that results in a dissertation or a series of articles in scientific journals. You can search for positions on research projects on offer or a position whereby you are free to submit your own research proposal. Read more on doing a  PhD  at Utrecht University. 

Other options

The best way to find a PhD position is through networking with the professor in the field you wish to specialize. Another option is to search via  www.academictransfer.nl . Here you can also find more information on doing PhD research in the Netherlands. 

Follow Utrecht University

Utrecht University Heidelberglaan 8 3584 CS Utrecht The Netherlands Tel. +31 (0)30 253 35 50

More From Forbes

What businesses can learn from the financial services industry contending with artificial intelligence.

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Tom Padgett is President of the Smarsh Enterprise business, which serves large global banks and financial services organizations.

Many companies are grappling with how to deal with artificial intelligence. However, given the regulatory scrutiny of financial services, the stakes are often higher. That puts a premium on leaders in the industry being able to cut through the hyperbole and find out how to reap the greatest rewards of this fast-evolving technology while minimizing the risks.

Some technology experts say that generative AI—which can create human-like responses to natural language queries—is the greatest invention since humans harnessed fire. Others warn that it's the beginning of our end. Then there's the group residing somewhere in the middle, and others who contend it's a fad.

Time will determine which view is correct. (As a leader in an organization that has been delivering AI-powered regulatory technology solutions for large global banks for over a decade, you can guess where my view aligns). What is certain is that no firm wants to become an example of how AI went wrong.

Approach AI as both a blessing and a curse.

The most immediate benefit of generative AI is creating efficiencies. I think every business, no matter the industry, should be looking for ways to maximize productivity and gain strong insights into its own data. Doing so can improve sales processes and HR functions, allow for better stakeholder communication or help recruit top talent.

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Financial services is no different. Yet what makes the industry unique is that firms are bound by regulations requiring the preservation and oversight of business communications—resulting in the storage of large amounts of data. Firms must also be able to defend and explain the AI used.

That's both a blessing and a curse. Storing massive quantities of sensitive communications data amounts to a huge responsibility. One misstep could result in a seven-figure regulatory fine, reputational damage or both. Yet, the upside is that the same data is immeasurably valuable when a layer of AI can be applied to understand patterns, behaviors and intent that can help the organization operate efficiently, reduce risk and make decisions faster—and more accurately.

Other businesses may not have the regulatory obligations that forced them to confront the same dynamic… yet. I think they should treat AI as if they do because how they manage their data and train their large language models could have enormous implications.

Proceed with caution.

Today, with AI beneficiaries driving huge gains for investors, many organizations want to claim they have the most cutting-edge AI solutions. In the rush to do that, some skip an essential step: protecting themselves.

So, what happens is they adopt random, knee-jerk steps that get them close to where they want to be as quickly as possible. A better plan is to build a long-term AI strategy that safeguards the business, partners and, importantly, customers.

Firms have a duty to investors and the global financial system to use AI responsibly. AI is a serious opportunity with serious implications. If done too quickly, or with the wrong partner, a lackluster implementation of AI could spell disaster.

Companies must be very selective about who they work with as they seek to develop innovative solutions. No business is likely to build all the necessary tools in-house while staying focused on its core competencies.

Watch out for red flags.

AI is imperfect, especially at this stage in its development. For instance, AI-generated stock recommendations pose a significant risk in financial services, with companies having to validate them, which is a challenge. Businesses in other industries no doubt have similar hurdles.

Also, generative AI can produce outcomes based on confidential information about customers and employees, such as names, addresses and social security numbers. Making sure this private data is excluded from a model’s data set requires robust disclosures and guardrails.

Another issue is us: humans. Since generative AI typically relies on restricted data pools, it is possible to game it to produce false, misleading and irrelevant outcomes despite many of them initially seeming valid.

Additionally, people can skew the original training data or algorithms. Experts say biases can lead to generative AI sometimes associating women and racial minorities with common, often negative stereotypes.

Beyond the technology, another concern is "AI whitewashing." In an era when the term "artificial intelligence" has become a buzzword, some companies fall into the trap of telling the market what it wants to hear.

Executives must resist the urge to exaggerate the relevance of their company's data pools and the capabilities of their generative AI tools because those things could land them in regulatory hot water. The SEC, in fact, already has warned firms against making false and misleading statements.

Balance and responsibility are key right now.

Chief technology, security, compliance and legal officers generally make the decisions regarding company data and AI. Now, more of the C-suite, including CEOs at some of the largest global companies, are being kept up at night with the challenges and opportunities of the game-changing technology.

The reason for this shift is closely tied to its promises of achieving more with less and the perceived business potential. On the flipside, AI has made managing risk much more complex. For one, the sheer amount of data demands a sound strategy, especially for highly regulated businesses like financial services.

AI has endless possibilities, with the potential to save companies millions of dollars and supercharge growth. That said, numerous landmines exist. How leaders deal with them will go a long way toward determining how their firms perform in the years to come.

Executives now must balance regulatory pressure and uncertainty, investor confidence and firm best interest, and scaling innovation amid a sea of “AI whitewashing.” It’s a complex challenge, one that financial services is uniquely positioned to tackle before the rest of the world catches up.

This is a critical moment for organizations to decide how, when and why to use AI, but they must do so responsibly. Those who do it right will win. Those who don’t may cease to exist.

Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?

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Artificial intelligence blurs the line between satire, misinformation

by GRAYCE MCCORMICK | The National Desk

HUNT VALLEY, Md. (TND) — We are less than 100 days from Election Day, and the news cycle has had no shortage of headlines that will impact voters’ decisions in November.

From an assassination attempt on former President Donald Trump to a shaky debate performance by President Joe Biden that ultimately led to him dropping his re-election bid, political content has proliferated across all social media platforms – and along with it, misinformation.

Claims that Trump’s assassination attempt was “ staged ,” false reports that former President Jimmy Carter died, false reports that Biden died – all these and more conspiracy theories have spread across the internet.

Over the weekend, tech billionaire and “X” owner Elon Musk shared a post on his X account of a video of Vice President Kamala Harris saying things she didn’t actually say. An artificial intelligence voice-cloning tool mimicked her voice, and made her say that she’s the new Democratic candidate for president because, “Joe Biden finally exposed his senility at the debate,” and she’s a “diversity hire” who doesn’t know the “first thing about running the country.”

After some backlash, Musk clarified the video is meant to be satirical and that parody is not a crime. Users still question whether his post could violate his own company’s policy of not sharing “synthetic, manipulated or out-of-context media that may deceive or confuse people and lead to harm.”

But the policy still allows for memes and satire as long as there’s not so much confusion about whether or not it’s real.

This situation – and many others like it – has experts questioning where the line is between “definitely fake” and “definitely real.”

“If people are believing it’s true, then it’s misinformation,” said Jack Brewster, an enterprise editor at NewsGuard, a media outlet that tracks and debunks false narratives online. “But if people are not, then it’s satire. So, in that case, there’s somewhat of a blurred line already.”

Joke or not, many of these deepfakes can be convincing.

Take the fake Biden robocall from January, where a robocall impersonating Biden told voters not to turn out in New Hampshire’s primary. The consultant behind the robocall said he was just trying to show how convincing and misleading AI can be, but he now faces a $6 million fine from the Federal Communications Commission and 26 criminal counts in New Hampshire courts.

That consultant faces consequences for the confusion, but many online users don’t, and these AI tools are essentially at every single person’s fingertips.

After Trump was shot at a rally on July 13, a photo circulated on X showing a Secret Service agent, who was holding a bloodied Trump up, with a smile on his face. The photo was AI-edited; in real life, the agent had a serious look on his face.

“It’s so easy to make these deepfakes in real time,” Brewster said. “There are now tools that are just free on the internet for people to make AI audio voices and change videos in real-time. I think that’s a major departure from the 2020 election when this technology existed, but wasn’t widely accessible.”

If every American can whip up a deepfake (or a “cheapfake,” which is the same thing as a deepfake but using more traditional editing methods like photo editing software and not AI), so can every foreign adversary. According to the FBI, Russia was using bots and spreading polarizing misinformation back in 2016, and the technology has only improved since then.

The U.S. intelligence community believes Russia will be a key player in trying to interfere in the November election. Officials say Russians are already spreading propaganda online through a series of third-party companies that pose as marketing firms.

These companies operate dozens of fake news websites aimed at Western readers and create armies of fake social media accounts to promote them. Brewster says NewsGuard has come across plenty of these fake sites as well.

Intelligence officials believe Russia will try to use its propaganda efforts to support Trump over Harris in the presidential election. They say Iran will try to attack Trump, and China doesn’t appear to have a preference but may try to interfere in congressional races.

So, is there anything we can do to stop it?

Some social media companies like Meta and Microsoft have banned AI-generated political ads, and about 20 states have passed laws regulating fake content.

But Congress is late to the party. Lawmakers have considered several bills and passed a few, but none on the issue of regulating AI-generated political content. Experts say law always moves much slower than innovation, and at this point, people are developing new ways to use AI so quickly that any laws passed now could become irrelevant.

The FCC is proposing a first-of-its-kind rule that would require TV and radio advertisements to disclose whether they used AI-generated content, and so is the Federal Elections Commission.

But, in either case, these rules aren’t likely to go into effect until after Election Day. And, they don’t cover social media – platforms that might be used just as much, if not more, than TVs and radios nowadays.

“The era in which we exist is one where reputable news outlets are on the same platform with everyone else,” Brewster said. “Of course there’s going to be blurred lines between what people should believe is a satire, and what people should believe as news, because you’re not really sure in what capacity someone is sharing information. Everyone is kind of on the same footing.”

So, how do you know what’s real and what’s fake?

There are AI detection tools out there, but Brewster said he wouldn’t recommend any of them, because he’s still skeptical of their effectiveness. Plus, people are making their own AI chatbots, so detection tools may not work on those.

He recommends three steps if a user comes across a piece of content they’re not sure about:

• Check the source spreading the claim, and see if it’s legitimate
• See if an article has been written about that content by a journalist or a fact-checker
• And look for telltale signs yourself – inconsistencies in photos like a blurry mouth or a mismatched hand.

Still, even with heightened media awareness, Brewster sounds the alarm about the widespread skepticism AI is creating in people. While much of the focus is on trying to weed out photos or videos that are fake, many people are questioning things that are actually real and true.

People doubted Trump was shot; people doubted Biden had Covid-19 and that he dropped out of the race. Many are so skeptical of what’s real and what’s fake, that they might be choosing to not believe anything they hear or read or see.

“The problem AI has caused is that it’s muddied the water so now people don’t know when real events have or have not been doctored.”

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A parody ad shared by Elon Musk clones Kamala Harris’ voice, raising concerns about AI in politics

FILE - Tesla and SpaceX CEO Elon Musk addresses the European Jewish Association’s conference in Krakow, Poland, Jan. 22, 2024. A manipulated video that mimics the voice of Vice President Kamala Harris to impersonate her is raising concerns about the power of artificial intelligence to mislead just three months away from November’s elections. The video gained attention after Musk shared it on his social media platform X on Friday, July 26, without noting it was originally released as parody. (AP Photo/Czarek Sokolowski, File)

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NEW YORK (AP) — A video that uses an artificial intelligence voice-cloning tool to mimic the voice of Vice President Kamala Harris saying things she did not say is raising concerns about the power of AI to mislead with Election Day about three months away.

The video gained attention after tech billionaire Elon Musk shared it on his social media platform X on Friday without explicitly noting it was originally released as parody.

By late Sunday, Musk had clarified the video was intended as satire, pinning the original creator’s post to his profile and using a pun to make the point that parody is not a crime.

The video uses many of the same visuals as a real ad that Harris, the likely Democratic president nominee , released launching her campaign. But the fake ad swaps out Harris’ voice-over audio with an AI-generated voice that convincingly impersonates Harris.

“I, Kamala Harris, am your Democrat candidate for president because Joe Biden finally exposed his senility at the debate,” the AI voice says in the video. It claims Harris is a “diversity hire” because she is a woman and a person of color , and it says she doesn’t know “the first thing about running the country.” The video retains “Harris for President” branding. It also adds in some authentic past clips of Harris.

Mia Ehrenberg, a Harris campaign spokesperson, said in an email to The Associated Press: “We believe the American people want the real freedom, opportunity and security Vice President Harris is offering; not the fake, manipulated lies of Elon Musk and Donald Trump.”

The widely shared video is an example of how lifelike AI-generated images, videos or audio clips have been utilized both to poke fun and to mislead about politics as the United States draws closer to the presidential election. It exposes how, as high-quality AI tools have become far more accessible , there remains a lack of significant federal action to regulate their use, leaving rules guiding AI in politics largely to states and social media platforms.

The video also raises questions about how to best handle content that blurs the lines of what is considered an appropriate use of AI, particularly if it falls into the category of satire.

The original user who posted the video, a YouTuber known as Mr Reagan, disclosed from the beginning both on YouTube and on X that the manipulated video is a parody. Yet Musk’s initial post with the video, which had far wider reach with 130 million views on X, according to the platform, only included the caption “This is amazing” with a laughing emoji.

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Over the weekend, before Musk clarified on his profile that the video was a joke, some participants in X’s “community note” feature suggested labeling his post as manipulated. No such label has been added to it, even as Musk has separately posted about the parody video.

Some users online have questioned whether his initial post might violate X’s policies , which say users “may not share synthetic, manipulated, or out-of-context media that may deceive or confuse people and lead to harm.”

The policy has an exception for memes and satire as long as they do not cause “significant confusion about the authenticity of the media.”

Chris Kohls, the man behind the Mr Reagan online persona, pointed an AP reporter to a YouTube video he posted early Monday responding to the ordeal. In the YouTube video, he confirmed he used AI to make the fake ad and argued that it was obviously parody, with or without a label.

Musk endorsed Trump, the Republican former president and current nominee, earlier this month. He didn’t respond to an emailed request for comment.

Two experts who specialize in AI-generated media reviewed the fake ad’s audio and confirmed that much of it was generated using AI technology.

One of them, University of California, Berkeley, digital forensics expert Hany Farid, said the video shows the power of generative AI and deepfakes.

“The AI-generated voice is very good,” he said in an email. “Even though most people won’t believe it is VP Harris’ voice, the video is that much more powerful when the words are in her voice.”

He said generative AI companies that make voice-cloning tools and other AI tools available to the public should do better to ensure their services are not used in ways that could harm people or democracy.

Rob Weissman, co-president of the advocacy group Public Citizen, disagreed with Farid, saying he thought many people would be fooled by the video.

“I’m certain that most people looking at it don’t assume it’s a joke,” Weissman said in an interview. “The quality isn’t great, but it’s good enough. And precisely because it feeds into preexisting themes that have circulated around her, most people will believe it to be real.”

Weissman, whose organization has advocated for Congress, federal agencies and states to regulate generative AI, said the video is “the kind of thing that we’ve been warning about.”

Other generative AI deepfakes in the U.S. and elsewhere have tried to influence voters with misinformation, humor or both. In Slovakia in 2023, fake audio clips impersonated a candidate discussing plans to rig an election and raise the price of beer days before the vote. In Louisiana in 2022, a political action committee’s satirical ad superimposed a Louisiana mayoral candidate’s face onto an actor portraying him as an underachieving high school student.

Congress has yet to pass legislation on AI in politics, and federal agencies have only taken limited steps, leaving most existing U.S. regulation to the states. More than one-third of states have created their own laws regulating the use of AI in campaigns and elections, according to the National Conference of State Legislatures.

Beyond X, other social media companies also have created policies regarding synthetic and manipulated media shared on their platforms. Users on the video platform YouTube, for example, must reveal whether they have used generative artificial intelligence to create videos or face suspension.

The Associated Press receives support from several private foundations to enhance its explanatory coverage of elections and democracy. See more about AP’s democracy initiative here . The AP is solely responsible for all content.

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Will Artificial Intelligence Replace Academic Editors?

Artificial intelligence (AI) is increasingly being used in academia. Many institutions now have guides for best practice and acknowledge that it can be used ethically to streamline many research processes. However, although it has many uses, its full expansion into academia appears to be a long way off.

Here, we look at how capable AI is of editing academic work in comparison to human academic editors.

How does AI edit text?

Artificial intelligence is highly competent at certain aspects of editing. It can make accurate changes to some texts to improve grammar, phrasing, and spelling.

It does this through natural language processing techniques, which involve analysing a large number of texts and synthesising responses based upon the identified relationships between the studied words and phrases.

By analysing large volumes of text and through the use of machine learning methods, the AI editor can make sophisticated changes based on the copious amounts of data it’s trained on. In short, it makes predictions of the word that is likely to appear in a text based upon these learned patterns.

How does AI compare to academic editors?

Whilst AI can be used to improve certain texts, it tends to be less effective for long, complicated works, such as those published in academia. Its effectiveness also depends on the level of editing required— whilst it can partially fulfil standard language editing and proofreading requirements , it is much less effective at making detailed edits and critiquing work.

Specialist knowledge

Academic editing services delivered by PhD-holding specialists with expertise in the author’s field offer much more than AI. Artificial intelligence programmes cannot currently earn a degree or specialise in any one field. Thus, the feedback they provide is generic and undetailed, and they have even been shown to be wildly incorrect in many instances. This could badly affect the integrity of the research and potentially cause the spread of misinformation.

Academic editors, comparatively, are experts in their fields and can thus make intelligent changes. For example, they can review the strength of arguments, detect and correct misinformation, and provide detailed feedback to improve many different aspects of the article. In short, they can significantly improve a research paper due to their having an advanced degree and knowledge of the field.

Sentence construction

Additionally, the use of AI chatbots such as ChatGPT is currently relatively easy to detect. In its current iteration, ChatGPT’s writing style is very distinct in that it tends to generate unconventional phrases. Thus, heavy intervention is generally required by a human when it has been used to write or edit academic content.

Such programmes have a writing and editing style that is technically correct, but it often constructs phrases that wouldn’t be used by people when writing or speaking. By using a human editor, researchers can be confident in the quality of their prose and also avoid mistakes indicative of AI involvement.

Creativity and adaptability

As expected in academic research, there are many complex challenges associated with writing and presenting scholarly material. Human editors are much more versatile than AI, capable of offering insights on many different components of research—for example, data presentation, compliance with ethical and formatting guidelines, structure and flow, accuracy of terminology, coherence of arguments, etc.

The ability to find creative solutions to difficult problems is what ultimately sets humans apart from AI editors. Whereas AI is confined to algorithmically determining the best solution, humans can use their knowledge and past experiences to adapt to unique situations, better enhancing research and improving its impact. This focused and tailored approach to editing is something that AI still seems to be a reasonable distance from being able to do.

Could AI be used as an alternative to academic editors?

Due to AI not yet being capable of critical thinking, it is currently deemed unsuitable as a replacement for academic editors who can make tailored suggestions and significantly improve many aspects of academic papers.

No existing AI programmes can provide nuanced feedback  and logical consistency cannot be ensured, as they are currently generalists that can provide surface-level insights on a broad range of topics. Thus, academic editors are not currently replaceable as professional specialists in their fields, and AI should ideally only be used to streamline certain tasks during the publication process.

MDPI Author Services offers several academic editing services to prepare your research for publication. Our Academic Service is our most comprehensive, providing two rounds of editing and quality control checks (one language and one specialist), a detailed report compiled by the Academic Editor reviewing all sections and other aspects of your paper, and more.

The Author Services team is made up of highly skilled English editors who have edited over 70,000 papers, with a 97% author satisfaction rate. Our services are available to both MDPI authors and those publishing with other journals. Visit the link above to get a free quote today.

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Students and Professors Believe AI Will Aid Cheating

A new survey finds students believe it’s already easier to cheat, thanks to generative artificial intelligence—and instructors think it will get worse in coming years.

By  Lauren Coffey

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Coursera is the latest to launch a tool for detecting the use of AI in student work.

While instructors and students see the potential of generative artificial intelligence—which can be used for everything from creating rubrics to getting study-guide help—they also see the potential for a rise in cheating aided by the technology.

According to a report released today and shared first with Inside Higher Ed by publishing firm Wiley, most instructors (68 percent) believe generative AI will have a negative or “significantly” negative impact on academic integrity.

While faculty concerns about the use of AI to cheat are nothing new, the study also polled more than 2,000 students—who agreed that generative AI will boost cheating potential. Nearly half of them (47 percent) said it is easier to cheat than it was last year due to the increased use of generative AI, with 35 percent pointing toward ChatGPT specifically as a reason.

The numbers were not particularly surprising to Lyssa Vanderbeek, vice president of courseware at Wiley. “Academic integrity and cheating have been around forever,” she said. “It’s not surprising that it’s increased because of the fast evolution of these generative AI tools and their wide availability, but it’s not a new challenge; it’s been around for a long time.”

It’s important to note that the survey—which polled 850 instructors along with the 2,000-plus students—did not specifically define “cheating,” which some could view as fact-checking an assignment while others think it would only include writing an entire paper through ChatGPT.

Vanderbeek said she has seen a more open dialogue about cheating between professors and students in the classroom.

A Wiley survey shows students and faculty both expect to see cheating increase in the next three years, aided by generative AI.

“One thing we’ve noticed with instructors is discussing in the classroom what cheating is and normalizing getting help—and looking at productive ways to get help,” she said, “versus it being, ‘Where do you cross the line with cheating?’”

When OpenAI’s ChatGPT first hit the scene in November 2022, it immediately drew concerns from academics that believed it could be used for cheating. In an Inside Higher Ed survey released earlier this year , nearly half of university provosts said they are concerned about generative AI’s threat to academic integrity, with another 26 percent stating they are “very” or “extremely” concerned.

Many institutions were quick to ban the tools when they first launched but have loosened the restrictions as the technology—and attitudes toward it —has evolved over the past 18 months.

Technology and academic experts have often drawn comparisons to similar fears that emerged when Wikipedia was first released in 2001, or in the 1970s when calculators were first widely introduced into classrooms.

In the Wiley survey, a majority of professors (56 percent) said they did not think AI had an impact on cheating over the last year, but most (68 percent) did think it would have a negative impact on academic integrity in the next three years.

On the flip side, when asked what made cheating more difficult, more than half (56 percent) of students said it was harder to cheat than last year due an uptick in in-person classes and stricter rules and proctoring. Proctoring saw an uptick during the pandemic when courses became remote, and many institutions have kept the practice as classes shifted back to face-to-face.

Students believe it is easier to cheat in class compared to last year, largely due to generative AI and ChatGPT.

Students who stated a strong dislike for generative AI cited cheating as the top reason, with 33 percent stating it made it easier to cheat. Only 14 percent of faculty cited the potential for cheating as a reason for disliking the technology, with their top reasoning (37 percent) being that the technology has a negative impact on critical thinking.

Vanderbeek said she was surprised at the number of students who simply did not trust AI tools—with 36 percent citing that as a reason they don’t use them. Slightly more (37 percent) said they did not use the tools due to concerns their instructor would think they were cheating if they used AI.

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And as reported in previous surveys , including Inside Higher Ed ’s 2024 provosts’ survey , student use of generative AI greatly outpaced faculty use—45 percent of students used AI in their classes in the past year, while only 15 percent of instructors said the same.

Vanderbeek said there are three main approaches institutions can take when looking at keeping academic integrity intact: creating incentives throughout the work process, like giving credit for starting early; introducing randomization on exams so it is harder to find answers online; and providing tools to instructors to identify “suspicious” behavior, like showing copied-and-pasted content or content submitted from overseas IP addresses.

“The takeaway is that there is still a lot to learn,” Vanderbeek said. “We see it as an opportunity: There are probably ways generative AI can help instructors provide learning experiences that they just can’t right now.”

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AcademyHealth CEO and President Aaron Carroll Builds on his Vision for the Future of HSR with a Focus on Funding and Artificial Intelligence

President and CEO Aaron Carroll discussed his vision for health services research at the June 2024 National Advisory Council Meeting (NAC) for the Agency for Healthcare Research and Quality (AHRQ). This second post in the series focuses on his priorities in creating more flexible funding mechanisms, artificial intelligence, and advanced data computing in digital health care research.

This blog summarizes remarks shared by AcademyHealth’s President and CEO, Aaron Carroll, at the latest National Advisory Council Meeting (NAC) for the Agency for Healthcare Research and Quality (AHRQ) meeting on his vision for the future of health services research (HSR). These remarks continue from yesterday’s blog outlining key priorities for HSR that NAC can consider in its recommendations to AHRQ. Today’s focus builds on the discussion of innovation, equity, and incentives with an eye toward how we fund HSR and the ways new technologies like artificial intelligence (AI) may change our work.

Creating More Flexible Funding Mechanisms to Foster Cross-Sector Activities 

One of the most innovative initiatives that I have taken pride in learning about during my first three months at AcademyHealth is our Evidence-Informed State Health Policy Institute , which is comprised of three groups: the Medicaid Learning Directors Network, the State University Partnership Learning Network, and the Medicaid Outcomes Research Network. 

Through this Institute, AcademyHealth brings together several organizations that are doing HSR and policy research and provides a platform for them to learn from each other, share data, and troubleshoot new interventions. In many ways, this network operates like a force multiplier, allowing participants to leverage insights from work being conducted in one state or region and apply it across other relevant contexts. Although this type of work is much more impactful, it is not usually feasible due to limited allowances in R01s to fund large meetings and convenings. Conference grants have not increased in size in years while the cost of traveling and hosting meetings has risen. These barriers make attendance cost-prohibitive, especially for smaller organizations and programs with limited budgets. 

Increasing funding for regular network formation and convening, instead of funding one-off programs, should be a future direction. In the basic sciences for instance, R01s are renewed for years and years, which allows investigators to build labs and continue developing research products. We do not have those kinds of funding structures in health services research. Instead, HSR funding supports one-off programs or secondary database analysis but not much else. As a field we need to envision new and sustainable funding mechanisms and develop robust networks to continue research in the long-term.

Artificial Intelligence and Digital Healthcare Research

Discussions about the future of health services research cannot happen without acknowledging the role of AI and digital technologies in transforming our health care system. We have reached an inflection point with respect to AI. AHRQ has a long history of leadership in health information technology and data‐focused work, including the  Clinical Decision Support Innovation Collaborative , which comprises a diverse group of stakeholders at the forefront of using technology to better support care teams. This collaborative, which AcademyHealth supports, provides a good example of the type of learning network we will need as we move forward with artificial intelligence. We must focus on the harms of AI and not just the benefits—which we were not always successful at when it came to early research on information technology, computerized electronic medical records and computerized physician order entry.

We also need to recognize that there are multiple types of AI being deployed now, and clarity on the different types and their functions is critical for precise regulation. As an editor at JAMA Pediatrics, almost all the research that has come across our desks so far has focused on whether ChatGPT can be used to replace physician-patient correspondence. Artificial intelligence is going to be used in those ways, but it is also going to be used in more discrete, yet ubiquitous ways like the use of AI to conduct risk assessments, assist with prognosis decisions, determine how we care for patients, and identify patients ready for discharge. AI may also be leveraged to help health systems spend less while increasing revenue. Although lowering health care spending and making it more efficient is not inherently a bad thing, AI could encourage the wrong incentives and drive decision-making that prioritizes profits over people, which is incongruent with our overarching values for improving population health. 

In summary, as we talk about the future of HSR, we need to think about innovation in our methods and creativity in the topics we choose to generate evidence around. These considerations can help reinvigorate our messaging around the importance of HSR. In response to threats to defund health equity initiatives, it is important to maintain a focus on diversity, equity and inclusion in health services research. We need to expand our focus and funding programs to better support community partnerships which could help reimagine the skillsets necessary for HSR and actionable health system reform. Finally, we must be careful not to miss opportunities to inform the use and roll out of AI technologies and think about how we can most effectively study AI in the present to prepare for the future. These areas provide a real opportunity for our field to operationalize evidence and move policy forward that improves health and health care for all.