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We are pleased that you would like to write your bachelor/master thesis at our institute. On this website you may find information about topics, registration and general guidelines. We wish you much success!

Bachelor Theses

Master theses, notes for theses, contact for general questions about your thesis.

Bachelor theses in Statistics consist of a description of new statistical methods and their application on specific data.

We would like to ensure that all students of the Economics and Management bachelor receive a topic for their theses within standard period of study. Therefore, allocation is made by the Office of the Dean of Studies via a centralized application procedure . Afterwards, the Office of the Dean of Studies will inform you about the institute which you were assigned to.

We have prepared a selection of topics for students who were assigned to our institute. You can find a list with all current topics on our website. 

Topics for summer term 2024 as well as winter term 2024/25 will be assigned as desired within the available topics. Therefore, please contact the office with your desired topic by email.

As soon as you have told us about your chosen topic, we will reserve it for you.

We have arranged a selection of topics from different areas.

In the following, you may find a list of currently available topics which may help for your choice.

Heteroskedastizität

Im klassischen linearen Regressionsmodell wird eine konstante Varianz der Fehlerterme angenommen. Bei sich verändernder Varianz ist der Kleinste-Quadrate-Schätzer nicht mehr effizient. Getestet wird dies z.B. mit dem White-Test oder dem Breusch-Pagan Test (Original oder Koenkers Version). Als Lösung werden Heteroskedastizität-robuste Standardfehler oder die Gewichtete-Kleinste-Quadrate-Methode genutzt. Falls gleichzeitig Autokorrelation vorliegt, finden sogenannte HAC-Schätzer Anwendung (heteroscedasticity and autocorrelation consistent).

Einstiegsliteratur:

• J.M. Wooldridge. Introductory econometrics: A modern approach. Nelson Education, 2013 (Kap. 8 + 12)
• W.H. Greene. Econometric analysis. Pearson Education, 2012 (Kap. 9)

Endogenität

Im klassischen linearen Regressionsmodell ist eine Voraussetzung für die Konsistenz des OLS-Schätzers, dass die Kovarianz zwischen der Regressormatrix und dem Fehlerterm Null ist. Falls diese Annahme verletzt ist, liegt sogenannte Endogenität vor. Eine Folge davon ist, dass der OLS-Schätzer einen Bias besitzt. Eine Möglichkeit, um mit Endogenität umzugehen,ist die Verwendung von sogennanten Instrumentalvariablen. Diese werden mithilfe des Two Stage Least Squares (2SLS) Verfahrens geschätzt, um eine konsistente Schätzung der Koeffizienten zu erlangen. Das Thema kann um eine weitere Lösungsmöglichkeit für das Endogenitätsproblem erweitert werden: Das Prinzip der generalisierten Momentenmethode (Generalized Method of Moments, GMM) liegt in der Festlegung von Bedingungen für die Momente der unterstellten Verteilung der Störterme des Modells. Die zu schätzenden Parameter werden so gewählt, dass sie möglichst gut im Einklang mit den Bedingungen stehen.

• J.H. Stock und M.W. Watson. Introduction to Econometrics. Pearson Education, 2011 (Kap. 12)
• J.M. Wooldridge. Introductory econometrics: A modern approach. Nelson Education, 2013 (Kap. 15)
• W.H. Greene. Econometric analysis. Pearson Education, 2012 (Chap. 13)
• J.M. Wooldridge. “Applications of generalized method of moments estimation”. In: Journal of Economic perspectives 15.4 (2001), S. 87–100

Simultane Gleichungssysteme

Ein einfaches simultanes Gleichungssystem lässt sich dadurch charakterisieren, dass die abhängige Variable in der einen Gleichung als erklärende Variable in der anderen Gleichung vorkommt und umgekehrt. Daher entsteht ein Endogenitätsproblem. Zwei Probleme sollen in dieser Arbeit näher erläutert werden: Zum einen das Problem der Identifikation, d.h. unter welchen Umständen können die Koeffizienten beider Gleichungen geschätzt werden. Zum zweiten sollen Schätzer vorgestellt werden, die unter Endogenität funktionieren und die die Koeffizienten des Systems Gleichung für Gleichung schätzen.

• W.H. Greene. Econometric analysis. Pearson Education, 2012 (Kap. 10)
• F. Hayashi. “Econometrics”. In: Princeton University Press (2000) (Kap. 8)
• J.M. Wooldridge. Econometric analysis of cross section and panel data. MIT Press, 2010 (Kap. 8+9)

Treatment Effects

Beim Schätzen von durchschnittlichen Treatment Effects geht es darum den Effekt verschiedenster Maßnahmen wie z.B. einer Weiterbildung zu analysieren. Insbesondere besteht die Frage wie man diese Effekte möglichst genau messen und kausal interpretieren kann, falls keine komplett randomisierten Experimente durchgeführt wurden. Für diesen Fall können Matching Verfahren angwendet werden um möglichst ähnliche Einheiten in der Treatment- und der Kontrollgruppe miteinander zu vergleichen. In dieser Arbeit sollen die zwei prominentesten Matching Verfahren und ihre Eigenschaften vorgestellt worden: Covariate Matching und Propensity Score Matching. Beim Covariate Matching werden verschiedene Einheiten basierend auf ihren beobachtbaren Eigenschaften miteinander gematched. Währenddessen werden beim Propensity Score Matching die Einheiten basierend auf der Wahrscheinlichkeit, dass sie in die Treatment Gruppe gehören, gematched.

• G. Cerulli. Econometric evaluation of socio-economic programs. Springer, 2015 (Chap. 2)
• J.M. Wooldridge. Econometric analysis of cross section and panel data. MIT Press, 2010 (Chap. 21)

Random Forests

Entscheidungsbäume stellen ein leicht zu interpretierendes nichtparametrisches Verfahren dar. Allerdings sind sie in der Praxis oft zu variabel, weswegen meist auf eine Erweiterung, die sogenannten Random Forests zurückgegriffen wird. Diese basieren auf der Idee des Bootstraps. Aus der ursprünglichen Stichprobe wird mit Zurücklegen eine neue Stichprobe gezogen, für die dann ein neuer Entscheidungsbaum bestimmt wird. Dabei wird in jedem Schritt zufällig ausgewählt auf Grundlage welcher Regressoren Entscheidungen getroffen werden können. Dieser Vorgang wird viele Male wiederholt und die Vorhersagen der so entstandenen Bäume werden durch Durchschnittsbildung zu einem Modell zusammengefügt. Einstiegsliteratur:

• G. James u. a. An introduction to statistical learning. Springer, 2013 (Kap. 8)
• L. Breiman. “Random forests”. In: Machine learning 45.1 (2001), S. 5–32
• E. Scornet. “On the asymptotics of random forests”. In: Journal of Multivariate Analysis 146 (2016), S. 72–83

Das Perzeptron stellt den Grundbaustein moderner neuronaler Netze dar und wird zur Klassifikation verwendet. In seiner grundlegenden Funktionalität kommt das Perzeptron dem multiplen linearen Regressionsmodell gleich. Im Bereich der neuronalen Netze werden die unabhängigen Variablen des Modells als Eingabe in das Perzeptron interpretiert, welche abhängig von den gelernten Gewichten des Perzeptrons zu einer bestimmten Ausgabe führen. Das Lernen der Gewichte erfolgt über einen iterativen Trainingsprozess, dessen Funktionsweise und Limitationen im Rahmen dieser Arbeit vorgestellt werden sollen. In der Arbeit soll weiter auf das Problem der linearen Separierbarkeit der zu klassifizierenden Daten eingegangen und Lösungsmöglichkeiten wie das mehrlagige Perzeptron oder der Maxover-Algorithmus vorgestellt werden.

• W. Ertel und N.T. Black. Grundkurs K¨unstliche Intelligenz. Springer, 2016 (Kap. 8.2)
• C.M. Bishop u. a. Neural Networks for Pattern Recognition. Oxford University Press, 1995 (Kap. 3.5)
• F. Rosenblatt. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”. In: Psychological review (1958), S. 386

Hauptkomponentenanalyse

Die Hauptkomponentenanalyse, oder auch Principal Component Analysis (PCA), dient zur Identifizierung der Varianz-Kovarianz Struktur mittels Linearkombinationen aus den ursprünglichen Variablen. Die generelle Intention dieses Verfahrens dient der Komprimierung der Daten und der Interpretierbarkeit dieser. Bei der Anwendung der Hauptkomponentenanalyse werden häufig Zusammenhänge offengelegt, die vorher nicht offensichtlich sind und daher eine neue Interpretation der Datenstrukturen und Zusammenhänge innerhalb des Datensatzes ermöglicht. Aus diesem Grund wird die Hauptkomponentenanalyse hauptsächlich zur Erkennung von Beeinflussungsmustern und -strukturen in hochdimensionalen Datensätzen besonders im Bereich der Finanzwissenschaft, Data-Mining, Bioinformatik und der Umweltforschung eingesetzt.

• R.A. Johnson, D.W. Wichern u. a. Applied multivariate statistical analysis. Prentice Hall, NJ, 2002 (Chap. 8)
• A.J. Izenman. “Multivariate regression”. In: Modern Multivariate Statistical Techniques. Springer, 2013, S. 159–194 (Chap. 7)
• W.J. Krzanowski. Recent advances in descriptive multivariate analysis. Clarendon Press, 1995 (Chap. 5)
• M. Ringn´er. “What is principal component analysis?” In: Nature biotechnology 26.3 (2008), S. 303

Faktorenanalyse

Die Faktoranalyse behandelt multivariate beobachtete Variablen die zumeist von gleichen oder ähnlichen unterliegenden Variablen beeinflusst werden. Ähnlich wie die zu analysierenden Variablen sind auch die zugrundeliegenden Faktoren unterschiedlich für jedes beobachtete Individuum. Allerdings sind die zugrundeliegenden Faktoren unbeobachtbar. Jeder Faktor der verwendet wird erklärt einen Teil der Varianz in den beobachteten Variablen und wird danach geordnet, wie hoch der Anteil der erklärten Varianz von den einzelnen Faktoren ist. Das Ziel der Faktoranalyse ist die Dimensionsreduktion der analysierten Variablen. In der Arbeit sollen vor allem auf die Motivation für Faktormodelle, die Modelldefinition und Annahmen sowie Möglichkeiten der Parameterschätzung eingegangen werden.

•  A.C. Rencher und W.F. Christensen. Methods of Multivariate Analysis. John Wiley & Sons, Inc., 2012 (Chap. 13)
• J.F. Hair u. a. Multivariate Data Analysis. Pearson Education Limited, 2014 (Chap. 3)

Clusteranalyse

Die Clusteranalyse wird dazu verwendet um Daten, aus meist multivariaten Beobachtungen, anhand ähnlicher (Verhaltens-)Strukturen in sogenannte Cluster oder Gruppen zu ordnen. Das Ziel ist es optimale Gruppen für die Beobachtungen zu finden, sodass in jeder einzelnen Gruppe nur die Beobachtungen zusammengefasst werden, die eine ähnliche Struktur aufweisen, während die einzelnen Gruppen untereinander keine Ähnlichkeiten aufweisen. Um eine Gruppierung zu ermöglichen, gibt es verschiedene Algorithmen. Die einen betrachten alle Beobachtungspaare die auf Ähnlichkeit basieren. Dabei verwendet die Ähnlichkeitsanalyse eine sogenannte ”Measure of Distance”. Andere Algorithmen verwenden eine vorher festgelegte Clustermitte oder vergleichen die Variabilität der einzelnen Cluster mit- und untereinander. Das Anwendungsgebiet der Clusteranalyse ist vielfältig wie zum Beispiel Medizin, Soziologie, Kriminologie, Anthropologie, Archäologie, Geographie, Marktanalysen, Wirtschaftswissenschaften und Ingenieurwesen.

• A.C. Rencher und W.F. Christensen. Methods of Multivariate Analysis. John Wiley & Sons, Inc., 2012 (Chap. 15)
• J.F. Hair u. a. Multivariate Data Analysis. Pearson Education Limited, 2014 (Chap. 8)

k-Nearest-Neighbors

k-Nearest-Neighbors (k-NN) ist eine nicht-parametrische Klassifikationsmethode. Der Grundgedanke ist, einzelne Datenpunkte basierend auf der Klassenzugehörigkeit ihnen ähnlicher Datenpunkte - ihrer Nachbarn - zu klassifizieren. Neben der Definition von Entfernung spielt die Wahl des Parameters k, welcher die Größe der zu berücksichtigenden Nachbarschaft steuert, eine wichtige Rolle. In dieser Arbeit soll zunächst das Prinzip der Nächste-Nachbarn-Klassifikation und ihrer verschiedenen Ausprägungen vorgestellt werden, um darauf aufbauend die Wahl des Parameters k und der daraus folgenden Konsequenzen sowie die Evaluationsmöglichkeiten der resultierenden Klassifikation diskutieren zu können.

• W. Ertel und N.T. Black. Grundkurs K¨unstliche Intelligenz. Springer, 2016 (Kap. 8.3)
• C.M. Bishop u. a. Neural Networks for Pattern Recognition. Oxford University Press, 1995 (Kap. 2.5)

Autoregressive Prozesse

Eines der wichtigsten Modelle in der Zeitreihenanalyse ist der autoregressive Prozess (AR), bei dem Beobachtungen anhand von vergangenen Beobachtungen und einem Zufallsschock modelliert werden. Wenn die passende Modellordnung bekannt ist oder geschätzt wurde, also die Anzahl an zu berücksichtigenden vergangenen Beobachtungen, kann mit unterschiedlichen Methoden das Modell angepasst und zur Prognose genutzt werden. Interessant ist besonders die Eigenschaft der Stationarität des Prozesses. Einstiegsliteratur:

• M. Deistler und W. Scherrer. Modelle der Zeitreihenanalyse. Springer, 2018 (Kap. 5)
• K. Neusser. Zeitreihenanalyse in den Wirtschaftswissenschaften. Springer, 2009 (Kap. 2 + 5)

Master Theses in Statistics consists of a description of new statistical methods and their application on specific data. This is similar to bachelor theses. Furthermore, new statistical methods could be described in detail and more critically. Another option is to do an empirical study on a statistical problem.

For master theses allocation is made on student's requests. If you would like to write your master thesis at our institute, you may contact Prof. Dr. Sibbertsen by email.

Topics for master theses are very diverse. They range from methodical work (method presentation, method comparison, method development) to own empirical work (data collection and analysis) with references to all other economic elective courses.

Topic assignment takes place in coordination with you. We will gladly consider your suggested topics.

Below you may find informationen about requirements for bachelor theses as well as a template for LaTeX. Your bachelor thesis should be 15 pages long.

For LaTeX beginners we recommend to use the university’s Overleaf cloud service ( https://www.luis.uni-hannover.de/de/services/speichersysteme/dateiservice/cloud-dienste/overleaf/ ). To start with, our template can easily be uploaded as a new project. Otherwise, proper installation of MiKTeX ( https://miktex.org/ ) and a LaTeX editor ( https://www.texstudio.org/ ) is required.

Entries for the bibliography file can be copied from Google Scholar (Cite -> BibTeX) or generated with e.g. https://www.doi2bib.org/ . In case of many references, a software for reference management (e.g. Citavi, https://www.luis.uni-hannover.de/de/services/betrieb-und-infrastruktur/software-lizenzen/software-katalog/produkte/citavi/ ) might be useful.

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Coursework & Examination

 The Examination Office is responsible for the organisation of examinations for the study programmes. This includes: Examination dates and forms, registration for examinations, granting of admissions and non-admissions to examinations, withdrawal from examinations, issuing of grade sheets, acceptance of final papers, checking of deadlines and the issuing and handing over of certificates and notifications.

Your respective Examination Board (for Electrical Engineering and Information Technology, Computer Science or Teaching) is responsible for everything beyond this. There you can, for example, apply for an extension of the deadline for a thesis or clarify whether and how examination achievements from other courses or other universities (e.g. for semesters abroad) can be recognised. The hearing interviews are also organised by the Examination Board.

Information of the Academic Examination Office

regulations, dates, contact persons, etc., per degree programme

information on online registration

transcript of marks, course of studies, etc.

Examination dates

To the pages of the examination boards.

responsible for the degress programme Electrical Engineering and Information Technology, Power Engineering and Mechatronics (only B.Sc.; in German)

responsible for the degree programme Computer Science and Computer Engineering (only in German)

responsible for the subjects Computer Science and Electrical Engineering

Notes on the obligation to be present

There is no compulsory attendance at lectures and exercises . Students therefore do not always have to attend these courses. Nevertheless, it is advisable to be present at all times, as this is the only way to get all the important information. Attendance is compulsory for projects and laboratories .

If you are unable to come to an appointment with compulsory attendance, please notify the lecturer in advance.

selected types of assessment

A term paper is an independent written work of a subject-specific or interdisciplinary task.

A written examination is a written or electronic assessment completed under supervision.

A written examination is a written or electronic assessment completed under supervision. Written examinations can be conducted in parts according to the answer selection procedure (e.g. single-choice, multiple-choice).

The oral examination shall not take place in public in the presence of an observer who himself/herself possesses the qualification to be determined by the examination or an equivalent qualification. The main subjects of the examination performance are recorded in minutes. Students who wish to take the same examination at a later examination date, as well as other members of the university who claim their own legitimate interest, are to be admitted as listeners to oral examinations. This does not extend to advice and notification of the examination result to the person(s) to be examined. At the request of the person(s) to be examined, listeners are to be excluded.

In an academic research paper a subject-specific or interdisciplinary topic is addressed in theoretical, experimental or constructive respect and the developed solutions are presented and explained in a manner usual for the professional activity.

The scope of work is bindingly defined in the appendix (in months or hours). The topic of a student research project can be suggested by any professor in the fields of Electrical Engineering or Information Technology of the Faculty of Electrical Engineering and Computer Science. The topic of the project work is determined by the examiner after hearing the examinee. The task and a time schedule must be fixed with the issue of the topic. During the preparation of the paper the examinee is supervised by the examiner. The time from the issue of the topic to the delivery of the thesis is six months. The topic of a paper can only be returned once and only within the first eight weeks of the submission period.

Two copies of the paper must be submitted to the office appointed by the board of examiners by the deadline; the date of submission must be recorded. The progress of the work may also be taken into account in the assessment. As a rule, the paper is to be assessed within four weeks of its submission.

A course-accompanying examination (VbP) deals with a question relating to a specific course and is taken during the semester. A VbP can consist of several partial examinations; the number is to be limited to four partial examinations.

frequent course types

In a lecture mainly theory is taught. Lectures usually take place in a large room or lecture hall where many students are sitting. There is a lecturer (professor or scientific assistant of the professor) who explains the lecture material on the blackboard or with the help of a presentation. In the lecture you will learn the exam material, get information about the contents of the exam and about learning aids (textbooks, scripts, formula collections etc.).

Lecture hall exercises also take place in a large room with many students. Unlike a lecture, an exercise uses the material from the lecture. The trainer calculates exercises on the blackboard, which you should follow and write down. In contrast to a lecture, students can ask questions at any time. However, there is usually not enough time to work through the exercises in the lecture hall.

Group exercises take place in groups of a maximum of 30 students. Usually there is one exercise sheet per week, which is worked out together during the exercise. The exercise is done by students alone or in small groups. During this time, the exercise leaders answer questions and present the sample solution at the end of the exercise.

In a laboratory, the contents learned in lectures and exercises are applied practically. The students form small groups in which they carry out an experiment. Before each experiment, the supervisor gives the students a script to prepare for the experiment. At the beginning of the laboratory there is almost always a pre-test. The supervisor wants to find out whether all participants are well prepared for the experiment. If they are not sufficiently prepared, he can exclude participants from the experiment. This is followed by the execution of the experiment. During this process, the participants usually record measurement data that they are to evaluate later. The topics can be very different, e.g. a black box measurement of an electrical component and recording the corresponding characteristic curve. After evaluating the measurement data, the participants are often asked to write a test report stating what was processed during the test and what the results are. In a final post-test certificate your performance for the respective test is evaluated. Protocol, pre-test and the cooperation in the execution of the test are evaluated individually for each participant. A laboratory contains 8 to 10 different experiments, which the students have to pass.

The "Studium Generale" is a name for one or more freely selectable courses. These courses actually belong to other courses of study. The courses must either end with an examination or be a foreign language course of the Fachsprachenzentrum. You can find further information on the pages of the ET & INF examination boards (only in German).

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• University Entrance Examination

The examination to gain a subject-specific university entrance qualification

One way of embarking on a degree course at a university or university of applied science without the Abitur in Lower Saxony is by passing a subject-specific university entrance exam, the fachbezogene Hochschulzugangsberechtigung, after vocational training.

The exam, (also known as the Immaturen- or Z-Prüfung ) is taken for a degree programme of your choice, independent of your professional specialisation so far. As a rule, a lengthy period of preparation is necessary; this is offered by a variety of institutions providing adult education. For admission to the exam certain requirements must be met: as a rule, vocational training and subsequently at least two years of relevant professional activity.

The legal requirements for the exam are laid down in a regulation of the Lower Saxony Ministry of Education. The authority responsible for the exam is the Niedersächsisches Landesinstitut für schulische Qualitätsentwicklung (NLQ) in Osnabrück. The relevant internet pages provide information on the exam, addresses of preparatory institutions and the application form for admission to the exam.

• Information and forms of the Niedersächsisches Landesinstitut für schulische Qualitätsentwicklung (in German language only)
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Structure of the examination

The exam is divided into two parts, comprising a general and a specialised (subject-specific) section.

General Section (A-Section)

The general section exams are organised and administered by the preparatory institutions. The exams consist of the following:

• Written exams in the subjects German, English and Mathematics or a science subject (Physics, Chemistry or Biology), lasting three hours each,
• An oral exam lasting around 30 minutes on the general knowledge and abilities of the candidates with reference to cultural, political, social or economic topics.
• Candidates able to produce a Level B2 English certificate according to the Common European Framework of Reference for Languages (CEFR) are exempted from the relevant exam.
• The Fachhochschulreife (certificate of aptitude for specific and short-course higher education) is recognised in lieu of the general section; in other words it exempts candidates from the general exams.

Specialised section (B-Section)

The specialised section exams are organised and administered by the university where the candidate intends to study. The subject matter of the specialised section exams derives from the essential basics of the subject chosen, or in the case of a two-subject bachelor’s degree the first subject chosen.

In the specialised section two part exams are to be taken:

• As a rule a written paper under supervision (lasting 2 – 5 hours) or a written assignment with oral exam on the assignment, and
• an oral exam lasting around 45 minutes.

B-Section at Leibniz Universität Hannover

Candidates wishing to take the specialised section exams at Leibniz Universität must register for their exam at the coordination office in the Centre for Continuing Education ( Zentrale Einrichtung für Weiterbildung ).

General information on the B-Section exam and the registration form can be found on the relevant internet page.

• B-section exam at Leibniz Universität  (German language only)

Student Advisory Services can supply all prospective students with information on general admission requirements, conditions and the courses offered at Leibniz Universität. You can arrange an appointment  via the service hotline 0511 762-2020.

Contact details for the Immaturenprüfung

For contacts for the Immaturenprüfung in the Lower Saxony Examination Office and at universities in Lower Saxony see below:

Contacts at the Universities in Hannover

Coordination of the B-Section Thomas Bertram official in charge : Britta Jahn Zentrale Einrichtung für Lehre, Studium und Weiterbildung Abteilung Weiterbildung (ZEW) Schlosswender Str. 5 30159 Hannover Tel. +49 511 762-19108 E-Mail: [email protected]  

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Niedersächsisches Landesinstitut für schulische Qualitätsentwicklung

Information on the regulations, organisation and registration for the exam is available from the Niedersächsische Landesinstitut für schulische Qualitätsentwicklung (NLQ).

Address and Contacts:

NLQ Hildesheim: Keßlerstraße 52 31134 Hildesheim

Frau Reinhold Phone: 05121 1695 269

Sascha Manig Phone: 05121 1695 224 E-Mail: [email protected]

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Information and Forms - Withdrawal from examinations and extension of deadlines for the MA in Philosophy of Science

This page is an unofficial translation of Hinweise und Formulare - Prüfungsrücktritt und Fristverlängerung Masterstudiengang Philosophy of Science as of December 2023. In case of any inconsistency, the original version is decisive. (The attachments were originally bilingual)

For data protection and security reasons, applications and declarations can only be sent from your university e-mail address (@stud.uni-hannover.de). Mails sent from other e-mail addresses will be rejected!

Please complete the cancellation forms/applications below and send them as a PDF file by e-mail to [email protected] .

CANCELLATION OR WITHDRAWAL FROM EXAMINATIONS

• Cancellation of a written examination and a written examination with answering procedure is possible online up to 7 calendar days before the examination date.
• Cancellation of an oral examination or a practical sports presentation can be submitted directly to the examiners up to the day before the examination .
• Cancellation of all other forms of examination listed in Annex 2 is possible up to the start of the examination . The start of the examination is the time when the topic is handed out.
• An exception to this is a return of topics - for Bachelor's and Master's theses in accordance with §7 of the relevant examination regulations.

(see §15 of the relevant examination regulations)

If the cancellation has not been made within the respective deadline , important reasons for withdrawing from the examination can be immediately asserted to the responsible body according to §3 (Dean of Studies or Examination Board) (Studiendekan*in oder Prüfungsausschuss).

• In the event of illness , a medical certificate is required , among other things. You can submit this certificate with all other necessary information using the form "Declaration of withdrawal due to inability to take an examination due to illness".
• In the case of other important reasons , the form "Declaration of cancellation/extension of the processing time for important reasons (not due to illness)" can be used.

These forms are sample forms. The certificate can also be created informally , provided it contains the following points:

• The health impairments of the examinee and
• the resulting limitations of the examinee with regard to the examination in question.

Please note: If you would like to apply for an extension of the processing time for a Bachelor's or Master's thesis or term paper due to illness, a medical certificate must be attached to the form "Extension of the processing time due to illness-related inability to take examinations". The application for an extension must be submitted immediately after the reason for the delay has occurred.

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Small Laboratory Work

Formerly general metrological laboratory (aml).

The general metrological laboratory (AML) is intended to convey to the students the practical implementation of mechanical engineering and metrological problems with the help of various experiments. For this purpose, tests are carried out in small groups at the participating institutes of the mechanical engineering department and jointly evaluated. The various experiments are from the field of transport, production, combustion and measurement technology, so that a broad insight into possible technical problems can be given.

Implementation

The Institute of Measurement and Automatic Control carries out the metrological part of the AML in the winter semester, the mechanical part is carried out at the responsible institutes of the Department of Mechanical Engineering.

metrological laboratory part (Kopie 3)

1 Versuch im

to be carried out at the Institute of Measurement and Automatic Control

und 4 Versuche im

to be carried out at various mechanical engineering institutes

Registration & Overall Coordination

Registration takes place online via Stud.IP , coordinated by the TFD.

Responsible Contact Person:

Stefanie Lohse, M.Sc.

Telefon:   +49 511 762 2752 E-Mail:  [email protected]

Test Dates and Group Assignments

Test dates and group assignments are published on StudIP

Contact Person

Christian Neumann, Dipl-Ing. (FH)

Organizationally, the entire laboratory is managed by the Institute for Turbomachinery and Fluid Dynamics (TFD).

Experiment Implementation

The experiments take place in Garbsen. The dates are published in Stud.IP.

To carry out the experiment, it is necessary to read the script . There will be a small test at the beginning of the experiment to check that all participants have prepared for the experiment.

After the test, a protocol must be drawn up that must be submitted within one week of the test date. Please bring a USB data carrier for each group to take the test data with you.

In the event of illness or other justified reasons for the absence, the attempts must be made up on another date. Due to the Sars-CoV-2 pandemic , all participants must be healthy, i.e. free from coughs and fever. The valid distance rules must be strictly observed. All participants and supervisors please wear mouth and nose protection in the closed rooms.

• Mechanics IDS
• Transport and Automation Technology ITA
• Product Development IPeG
• Bionics in Assembly Technology Institute Name
• Microproduction Technology Institute Name
• Production Technology IFW
• Forming Technology IFUM
• Materials Science IW
• Technical Combustion ITV
• Turbo Machines TFD
• Process Technology IfT
• Acoustics in Turbo Machines TFD

Registration

Registration can only take place in groups of 6 people. The opportunity to form groups (separated into mechanical engineers and industrial engineers!) Arises during registration and should be carried out independently.

Personal presence is not required, the registration can also be carried out by other participants. In any case: bring your student ID and photo ID!

Each group of participants selects an experiment from the range of experiments from each experimental area (areas I - III). In order to enable as many students as possible to participate in the small laboratory work (formerly AML), experiment 4 is assigned to the respective groups of participants.

The schedule for the experiments, as well as the final group assignment, will be announced by the respective institute and at StudIP.

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Table of Contents

Jupyterhub for teaching in the cluster, availability, how to register and manage courses.

We provide an instance of the JupyterHub platform to support giving lectures in the LUH compute cluster environment. The JupyterHub server also runs the system NbGrader , which allows you to assign, distribute and (automatically) grade Jupyter notebooks. The JupyterHub platform is intended only for non-critical teaching activities that require LUIS cluster resources. If you want to use Jupyter notebooks for your own research, use the Open OnDemand web portal of the cluster instead.

JupyterHub is available via the URL https://jupyterhub.cluster.uni-hannover.de . If you want to connect from the “outside” (e.g. from Home), you'll have to establish a VPN connection to the university’s network first. See VPN Service for details.

Please make sure you have read and accepted the following notes for using the JupyterHub service in the LUIS compute cluster.

Usage notes for the JupyterHub service

Follow the link for the German version.

The JupyterHub service, which is operated as part of the LUIS "Scientific Computing" service, directly accesses the resources of the LUH computing cluster. Therefore, the rules for using the LUIS IT systems and services as well as the description in the cluster wiki apply.

The JupyterHub service is available under the same general conditions as the other resources of the LUH computing cluster. Continuous operation of the service cannot be guaranteed.

The JupyterHub service is unsuitable for taking exams. It is offered as a platform for non time-critical teaching activities in the cluster. The service may only be used for courses that require resources of the LUH computing cluster.

To get started with the JupyterHub platform, you will need to create cluster accounts using the BIAS system. Note that BIAS has recently added functionality to make it easier to create bulk cluster accounts. Talk to the BIAS team for further instructions. Accounts are required for both the instructors of a course and the students taking the course.

Note : We ask you to delete student accounts that are no longer needed after completing the course (using the BIAS system).

Before the accounts are granted access to the platform, you would need to initially register your course on JupyterHub. To do this, please contact the cluster team at cluster-help@luis.uni-hannover.de and provide the following information:

• Course name (the name should NOT contain a colon, “:”)
• Cluster username/account (and optionally first name, last name and email address) of at least one instructor of the course

A course may have multiple instructors, each with their own account/username. Each registered course receives a unique identifier in the form of the string course<3 digit number> .

After you receive notification that initial registration of the course is done, the course instructors can log into the JupyterHub platform at https://juputerhub.cluster.uni-hannover.de (see figure 1 )

and access the registered courses from the menu Available Courses , see figure 3 . To get to the menu page, click the button To Available Courses after logging in, figure 2 .

By selecting a course from the menu, the page displays the course settings, such as SLURM options, a list of instructors and students assigned to the course, location of course data in the cluster, etc. figure 4 . The information shown varies depending on whether the username is registered as an instructor or student with the selected course.

To start a Jupyter notebook for the selected course, click the link Start , see figure 4 . This submits a SLURM batch job to the cluster. The page opened will show the state of the job in the queue including the ID of the job, which is underlined in red in figure 5 . The time a course job waits in the queue depends on the job requirements and the current workload of the cluster.

Note : if a course job fails and you need support, please include the job ID in your request. However, since standard output and error streams of the job are directed to the ~/jupyterhub_<course id>_<job id>.log file in your $HOME directory, you may also examine the job log content yourself.

Note : If you want to reduce the wait time for a course job, some cluster resources may temporarily be reserved for the course. To arrange for this, please provide us with the required amount of resources (number of CPUs, memory, walltime and other options per course job and the number of course participants) as well as the start and end dates of the classes a few weeks before the course starts. As you may already know, as part of the FCH service , your institute may have your own compute nodes integrated into the LUIS cluster. These nodes may easily be reserved for your teaching activities, whereas for the generic ressources, we'll need to strike a balance.

To manage the registered courses, use the links > Export course config and > Import course config on the course menu page, figure 4 . The link > Export course config exports the current configuration of the selected course as a JSON file. The file name must be the course ID with the extension json . Open the JSON file and make appropriate modifications. To activate the changes, use the link > Import course config , which uploads the JSON file to the JupyterHub server. Note that the JSON file name must match the ID of the course whose configuration you want to change. The following course options can be modified:

• Course state
• List of instructors
• List of students
• Course SLURM job options (long form of the options must be entered)
• The name and location of the Conda environment used for the course
• Course start and end dates
• Course comment

The following in an example JSON configuration for the course with the ID course010 (save the lines to the file course010.json . Names are random):

The section "jupyter_notebook" can be removed from the configuration, as the parameters defined there ( "autosave_interval" , "kernel_default" ) are optional.

Each student and instructor entry above is a comma-separated string of the following items: "<username>,<first name>,<last name>,<email address>" . Where items are ordered, all of them except <username> are optional.

The course data directory (the parameter "location" in JSON file) is located at /jupyterhub/courses/<course id>/course_dir in the cluster, with <course id> being replaced by the corresponding course ID. The data directory is where the NbGrader database and config files, Jupyter notebooks and other working directories are placed. This path is fixed and cannot be modified.

The path to the course conda environment (the parameter "conda_env" ) should be prefixed with /jupyterhub/courses/<course id>/conda_envs . See the example configuration above. Path permissions are automatically set so that course instructors can read, create and modify, whereas enrolled students can read underlying files and directories. The conda environment assigned to a course will be accessible from the New menu of the Jupyter interface as a Jupyter kernel, the name of which is determined by the "name" parameter in the conda section of the course configuration, see figure 6 . Before starting a course job, make sure that the conda environment of the course is already created, otherwise the job will fail. See conda usage instructions in the cluster for details.

Instructors can also access their course locations from their home directory in the cluster as ~/MY_COURSE/<course name> .

By default, the following SLURM options are set for a course job: "job_options" : " --time=04:00:00 --nodes=1 --cpus-per-task=1 --mem-per-cpu=2G"

Note : The username can be registered in either the student list or the instructor list, but not in both simultaneously.

Note : Before uploading a configuration with new usernames to the JupyterHub server, make sure that the usernames exist in the system, i.e. they must be able to log in to the cluster in one of the possible ways .

Note : If you want to remove all student usernames from your course config, set "students" : " "

Note : The maximum number of student accounts per course is currently limited to 1,500.

To deactivate a course for students, set "state" : "inactive" in the course JSON file. Inactive courses do not appear in the student's course menu. By setting "state" : "removed" , you can make the course invisible in the course menu for both teachers and students. Note however that the course data are not removed but remain at their original location and therefore can still be accessed by instructors.

Once the course job starts, the Jupyter notebook server is launched on the allocated compute node. Instructors can manage (generate, release, collect, grade, etc.) assignments via the Formgrader menu in a Jupyter notebook session, see figure 7 .

Using the Assignments menu, figure 8 , in a Jupyter notebook session, students can retrieve and submit their course assignments.

Currently running course jobs are shown at the bottom of the main page, see section Running Course Jobs in figure 2 or figure 3 . You can connect to the running Jupyter session using the link connect . The link stop terminates a Jupyter notebook session (i.e. cancels the corresponding SLURM job).

NbGrader supports managing course assignments both interactively via a Jupyter notebook session as well as using the command line tool nbgrader . To get a command line, open the Terminal from the New menu of your jupyter session, see figure 9 . Once you open the terminal, you will be taken to the course directory, where you may start working with the tool immediately. The nbgrader command can also be executed from anywhere because the terminal environment is configured for the current course from which it was launched.

Details on how to create Jupyter notebooks for course assignments in NbGrader format and how to use NbGrader interfaces can be found here .

• My JupyterHub course does not start or ends with undefined errors. What are the possible reasons? Please check the file system quotas of your account - for example via SSH access to the cluster. If you have exceeded your limits, this would be one reason for the errors when starting the course.