Doctoral Course Program

The course is intended for doctoral students interested in applying MDP methods to sequential decision-making problems in Operations Management, and/or further developing the theory and models for MDP. The goal of the course is to enable students to model and solve (real-world) problems using the most recent MDP models and methods.

Syllabus: Advanced Markov Decision Processes: Constraints, Risks, and Predictions

Registration: The application starts on 25 June 2026, 12:00 noon. The deadline for application is 15 July 2026, end of day. Registration works on a first come, first served basis. Registration is only possible for doctoral candidates of TUM School of Management on the (provisional) doctoral candidates list.

Lecturer: Prof. Dr. Jingui Xie

Course dates: The course takes place in the first week of the TUM MGT Doctoral Summer School from September 14 to September 19, 2026.

Location: Campus Heilbronn

The course will introduce choice modelling techniques for consumer and marketing analysis. Starting with the theory of consumer choice, the course will discuss different data types available for choice analysis. It will then focus on the specifics of choice experiments, discussing the advantages and disadvantages of various experimental designs and data collection procedures. Participants will be familiarized with data handling and analysis, implementing multinomial logit, random parameters logit, and latent class analysis. To obtain an overview of the literature, participants will present papers from the relevant field.

Syllabus: Applied Choice Analysis

Registration: This course is offered as part of the Promotionskolleg Agrarökonomie. Registration occurs via https://www.agraroekonomik.de/

Lecturer: Prof. Dr. Jutta Roosen (TUM), Dr. Matthias Staudigel (TUM), Prof. Dr. Carola Grebitus (Arizona State University)

Course dates: June 22 – 26, 2026, Mo 13-16, Tu-Th 9-12 & 13-16, Fr 9-12

Location: The course is offered in Freising-Weihenstephan. Lecture is in person, attendance required.

This course is designed for doctoral students who plan to conduct quantitative analyses in their own research, want to strengthen their understanding of causal inference, and are interested in acquiring modern data science skills. It will also appeal to students who are curious about emerging approaches in causal AI and how graphical models can support more transparent and credible empirical research.

Syllabus: Causal Inference with Directed Acyclic Graphs (DAGs)

Registration: The application starts on 25 June 2026, 12:00 noon. The deadline for application is 15 July 2026, end of day. Registration works on a first come, first served basis. Registration is only possible for doctoral candidates of TUM School of Management on the (provisional) doctoral candidates list.

Lecturer: Prof. Dr. Paul Hünermund

Course dates: The course takes place in the first week of the TUM MGT Doctoral Summer School from September 14 to September 19, 2026.

Location: Campus Heilbronn

The course is intended for doctoral students interested in applying Operations Research methods to scheduling problems in Operations Management. The goal of the course is to enable students to model and solve (real-world) problems using the models and methods from the field of complex scheduling.

Syllabus: Complex Scheduling

Registration: Please send an informal registration e-mail to steinbergerch@tum.de.

Lecturer: Prof. Dr. Rainer Kolisch (TUM)

Course dates: The course will take place in the second lecture free period. 

Location: Campus Munich, Room 1517/19

This doctoral course aims at giving doctoral researchers at the School of Management an overview of the essentials quantitative methods. We will not focus on the econometrics, but aim at developing a better understanding of what the methods are all about and how they work in practice.

Syllabus: Econometrics I: Research Design and Estimation Methods

Registration: Apply via Email to the instructor until August 21st 2026.

Lecturer: Prof. Hanna Hottenrott (TUM)

Course dates: 8.10.2026, 9.10.2026, 12.10.2026, 21.10.2026, 22.10.2026, 23.10.2026

The first session in a day will start at approx. 9am and last to about noon, after a short lunch break it will continue from about 1pm to 2:30pm. The second session in a day will last from 2:45pm to about 4:30pm.

Location: Campus Munich

Participants will be assessed based on their seminar presentation (60%), homework assignments (20%), and oral contributions to the course (20%).

• Seminar presentations will be held by groups of two. Each group will present one method of addressing the problem of causal inference (e.g., RDD). The presentation of 120 min to 150 min shall introduce and explain the respective method as well as applications. Presenters will suggest an article in which this method is applied. The group will bring a dataset with which participants will apply the respective method during the course. The lecturer will meet with each group beforehand to aid in the preparation.
• Homework assignment. For some of the methods that we discuss in the course participants will do a homework assignment that consists of applying the method to a dataset, presenting the results, and providing an interpretation (in writing).
• Oral contributions. It is expected that participants prepare the readings for each session and are able to reflect on them. In addition, they shall actively take part in the discussion of the seminar presentations. 

The course is pass/fail, not graded. In order to pass the course, participants must take part in all classes. In case of excused absence due to illness they need to hand in a written assignment about the content of the class they have missed.

Syllabus: Econometrics II: Causal Inference

Registration: Until April 20, 2026, via email to dekai.xiao@tum.de.

Lecturer: Prof. Dr. Joachim Henkel (TUM)

Course dates: Kick-off Tuesday April 21, 2026, 3:00 pm – 5:30 pm in person, room 2544. The course will be taught as a series of seven half-day seminars between May and July. Dates will be coordinated with participants. The course is planned to be held in person. If you can’t attend in person (i.e. doctoral candidates from Heilbronn etc.), please mention this during registration.

Location: Campus Munich

Knowledge Objectives:

• Doctoral candidates will have a good understanding of academic writing and the structure of the different chapters in a scientific paper.
• Doctoral candidates will know efficient publication strategies

Skills Objectives:

• Doctoral candidates will have improved their skills in research as well as in writing their dissertations or papers.
• Doctoral candidates will have a solid draft of one of their dissertation paper.
• Doctoral candidates will have utilized the processes of revision by positive, constructive peer reviews of the documents by fellow doctoral candidates on their writing and by revising their documents.

Learning Objectives:

• Doctoral candidates will have learned about how to start and finish writing a scientific paper in the context of writing a doctoral thesis.
• Doctoral candidates will have identified standard elements of research papers and used this knowledge to work on their dissertations or papers.

Syllabus: Efficient Academic Writing for Empirical Research

Registration: Please apply until 30^th January 2026 to Theresa Treffers, theresa.treffers@tum.de, via email containing:

• Your CV with contact information (email and mobile) and chair of your dissertation
• Dissertation topic (1 sentence)
• Problem statement (3 sentences)
• Data description (3 sentences)
• Target journal (possibly call for papers) and/or conference with deadline

Lecturer: PD Dr. Theresa Treffers (TUM)

Course dates: 27.03.2026, 16.04.2026, 28.04.2026, 05.05.2026, 12.05.2026, 19.05.2026, 26.05.2026, 02.06.2026, 09.06.2026, 16.06.2026, 23.06.2026, 30.06.2026, 07.07.2026

Location: Munich and online

The course is aimed at doctoral students. The course is designed to equip doctoral students with foundational knowledge and practical insights into the role of generative artificial intelligence (AI) within social science research, with a particular focus on quantitative marketing research. Based on the current literature body, course participants evaluate and discuss current applications related to generative AI in marketing.

Syllabus: Generative AI for Social Science Research

Registration: Please send an email to contact.dm (at) mgt.tum.de to register for this course.

Lecturer: Prof. Dr. Jochen Hartmann (TUM)

Course dates: 22.04.2026, 07.05.2026, 18.06.2026, 02.07.2026, 06.08.2026 & 21.08.2026

Location: Online (MS Teams)

This course will give doctoral students an overview of state-of-the-art technologies and methods used in management research. The course will have a major focus on virtual reality in social science research will touch upon artificial intelligence and robotics. At the end of this course, participants will be familiar with the different methods and tools available for conducting research and will develop a concrete research project. We will discuss the benefits of using technologies, and the potential technical difficulties and drawbacks that might be encountered along the way, and how to solve them.

In this course, you will learn about existing methods and develop and work on a concrete research project involving innovative technology.

To this end, students will be asked to develop a final presentation showcasing a research project based on the concepts learned in the course.

Syllabus: Innovative Technologies in Management Research

Registration: By email to anely.bekbergenova@tum.de (Dr. Anely Bekbergenova) before August 17th. Participants will be admitted on a first come, first served basis.
In your email please include:
(1) Your doctoral research topics and motivations to follow the course
(2) A research question you would like to explore using innovative technology (e.g., virtual reality, artificial intelligence, robotics).

Lecturer: Dr. Anely Bekbergenova (TUM) and Prof. Dr. Claudia Peus (TUM)

Course dates: The course will be held in person at the TUM main campus in Munich (Arcisstr. 21, Building 0505, Room Z577)
Monday, 17.08.2026– 23:59 – Registration deadline
Tuesday, 15.09.2026 – 9:00 – 17:00 – Presentation by instructor
Wednesday, 16.09.2026 – 9:00 – 17:00 – Presentation by instructor & group/individual work
Wednesday, 23.09.2026 – 9:00 – 17:00 – Presentation by participants & feedback
Wednesday, 07.10.2026 – 23:59 – Research proposal deadline

Location: Campus Munich (Arcisstr. 21, Building 0505, Room Z577)

This course is for researchers on the doctoral or post-doctoral levels who are beginners in economic laboratory experiments. It will enable you to

• decide whether a laboratory experiment is appropriate to address some research question;
• find research questions in your area of interest that a laboratory experiment can address;
• develop an experimental design to address such a research question.

In addition, the course will offer you hands-on training on how to bring experiments to the laboratory. It will cover common practical issues, such as which software to use, how to recruit participants, or how to conduct an experiment.

The course will be most beneficial for you if you plan to run your own experiment soon. It will be particularly helpful for you if you consider using experimenTUM, TUM’s laboratory for experimental research in economics.

Syllabus: Introduction to Experimental Economics

Registration: Write to Andreas Ostermaier no later than July 13, 2026 to sign up (ostermaier@bwl.uni-kiel.de). Please state your primary research area and method. Mention also what motivates you to sign up for this course and whether you are planning to run an experiment.

If you have a research question or idea for an experiment that you would like to see as an assignment, remember to include a very brief proposal in your application. If you have any introductory readings, feel free to suggest these, too.

Please make sure you can attend the full course before signing up. From a pedagogical angle and out of fairness toward the other participants, you should not miss any part of the course for any reason, including the supervision of student exams.

Lecturer: Prof. Dr. Andreas Ostermaier (Kiel University)

Course dates: The seminar is scheduled to be held on July 20–22, 2026. The tentative schedule is as follows.

First day:

• Visit to experimenTUM (interactive);
• The ultimatum game (lecture & discussion);
• Standard designs (first assignment, interactive).

Second day:

• Standard designs cont’d (first assignment, interactive);
• Common design choices (lecture & discussion);
• Designing your own experiment (second assignment, interactive).

Third day:

• Designing your own experiment, cont’d (second assignment, interactive);
• Benefits and pitfalls of lab experiments (lecture & discussion);
• Discussion of participants’ projects.

The course will typically at 9 a.m. in the morning and end no later than 5 p.m. in the afternoon. The afternoons of the first and second days are partly reserved for working on your assignments. I will always be around to assist you, commuting between the teams.

Location: Campus Munich in room 0505.03.539

The course is aimed at doctoral candidates with an interest in Econometric Methods. The goal of this course is to introduce the concepts of Influence Functions, Local Robustness/Neyman Orthogonality and Machine Learning. The course will combine both general theory and applications (among others) to

• Two step estimation procedures
• Machine Learning Causal Inference
• Inequality of Opportunity
• Empirical Welfare Maximization

After the course students should be more familiar with how to use Machine Learning in economic applications with desired statistical guarantees. The course will be mostly focused on technical econometric aspects so the target audience comprises those more motivated by technical challenges.

Syllabus: Local Robustness and Machine Learning

Registration: Until 10th of June by email.

Lecturer: Joël Terschuur (TUM)

Course dates: June 22-26, 2026 from 10:00-12:00 and 14:00-17:00

Location: Campus Munich

The goal of this course is to present and discuss current state-of-the-art research in finance. Therefore, renowned researchers from various European universities will present their latest research in the fields of asset pricing, corporate finance, and financial intermediation. Extensive discussion in class is encouraged. Thereby, students will learn to know and discuss critically current topics in finance. They will also learn more about state-of-the-art research methodologies.

The target audience are doctoral candidates and post-docs in finance. A requisite for participation is a specialization in finance and/or accounting.

Syllabus: Advanced Topics in Finance Research

Registration: Please send an email to aida.cehajic@tum.de by October 10, 2025. In your email, please state your department and specify the topic and state of your doctoral thesis or research area (post-docs). Strict preference will be given to individuals with a specialization in finance and/or accounting.

Lecturer: Dr. Aida Cehajic (TUM)

Course dates: See https://www.fa.mgt.tum.de/fm/research-seminar/

Location: Campus Munich/hybrid

Econometric analysis aims at uncovering economic mechanisms, their causes and effects. Understanding the mechanisms behind a phenomenon is indispensable if one is to give advice to managers or policy makers, or to build theory. Simple regressions on cross-sectional data show associations, but not causality, so we need more sophisticated methods. This course shall convey econometric methods that allow causal inference, or at least to come closer to uncovering causal effects. The focus will be on applicable knowledge, less on details of the theory. The course is part of a series of econometrics courses at TUM School of Management that also comprises “Econometrics I: Research Design and Estimation Methods” by Professor Hottenrott and “Econometrics III: Advanced Econometrics and Machine Learning” by Professor Farbmacher.

Syllabus: Econometrics II: Causal Inference

Note: This course is normally taught in the summer semester but could not take place in the summer semester 2025 due to Prof. Henkel’s sabbatical. It is offered in the winter semester 2025/26 instead. It will also be offered in the summer semester 2026 to resume the usual schedule.

Registration: Probably Postponed to the summer semester 2026 Until October 13, 2025, via Moodle.

Lecturer: Prof. Dr. Joachim Henkel (TUM)

Course dates: Probably Postponed to the summer semester 2026 Kick-off Tuesday October 14, 2025, 4:00 pm – 6:30 pm in person (room will be announced). The course will be taught as a series of seven half-day seminars between November and February. Dates will be coordinated with participants. The course is planned to be held in person.

Location: Campus Munich - The course is planned to be held in person (room will be announced).

The course is part of a series of econometrics courses at TUM School of Management that also comprises “Econometrics I: Research Design and Estimation Methods” by Prof. Dr. Hanna Hottenrott and “Econometrics II: Causal Inference” by Prof. Dr. Joachim Henkel.

The course covers a selection of state-of-the-art methods in econometrics. It aims to provide students with a sound understanding of the methods discussed, such that they are able to do research using modern econometric techniques, as well as critically assess existing studies.
 

In particular, the course will cover the following topics:
• Generalized Methods of Moments (GMM) Estimation
• Potential Outcomes and Treatment Effects
• Panel Data Estimation
• Regression Shrinkage Methods (Ridge, Lasso, Elastic Net)
• Advanced Identification Strategies (e.g. Double Machine Learning and Causal Forests)
 

In the morning, we will briefly discuss the econometric methods (including some applications to illustrate them). Students will then apply these methods and will replicate recent research papers in economics. I will also assign a (replication) project to each student. You can also come up with an own application and/or dataset you are interested in.

Syllabus: Econometrics III: Advanced Econometrics & Statistical Learning (only PhD)

Registration: Until end of February, via email.

Lecturer: Prof. Dr. Helmut Farbmacher (TUM)

Course dates: March 23-27, 2026 (10am to 5pm)

Location: Campus Munich (room 2544)

Knowledge Objectives:

• Doctoral candidates will have a good understanding of academic writing and the structure of the different chapters in a scientific paper.
• Doctoral candidates will know efficient publication strategies

Skills Objectives:

• Doctoral candidates will have improved their skills in research as well as in writing their dissertations or papers.
• Doctoral candidates will have a solid draft of one of their dissertation paper.
• Doctoral candidates will have utilized the processes of revision by positive, constructive peer reviews of the documents by fellow doctoral candidates on their writing and by revising their documents.

Learning Objectives:

• Doctoral candidates will have learned about how to start and finish writing a scientific paper in the context of writing a doctoral thesis.
• Doctoral candidates will have identified standard elements of research papers and used this knowledge to work on their dissertations or papers.

Syllabus: Efficient Academic Writing for Empirical Research

Registration: Please apply until 15th September 2025 to Theresa Treffers, theresa.treffers@tum.de, via email containing:

• Your CV with contact information (email and mobile) and chair of your dissertation
• Dissertation topic (1 sentence)
• Problem statement (3 sentences)
• Data description (3 sentences)
• Target journal (possibly call for papers) and/or conference with deadline

Lecturer: Dr. Fabian Ahrens (TUM), PD Dr. Theresa Treffers (TUM), Prof. Dr. Isabell Welpe (TUM)

Course dates: 07.10.25, 20.10.25, 28.10.25, 04.11.25, 11.11.25, 18.11.25, 25.11.25, 02.12.25, 09.12.25, 16.12.25, 13.01.26, 20.01.26, 27.01.26

Location: Munich and online

The course is aimed at doctoral candidates and advanced Master’s students from all management disciplines including Leadership and Organizational Behavior, Human Resource Management, Corporate Social Responsibility, Marketing and Advertising, Supply Chain Management, Financial Management and Accounting, Strategic Management.

Syllabus: Empirical Studies in Ethical Decision-Making – Multi-method approaches

Registration: Write to Gari Walkowitz no later than January 30, 2026 to sign up (gari.walkowitz(at)tum.de). Please state your primary research area and main methodological approach. Mention also what motivates you to sign up for this course, and whether you plan to run an empirical study with an ethical reference. If you have an ethics-related research question or idea for a study design that you would like to see as an assignment, include a brief proposal in your application. If you have any introductory readings, feel free to suggest these, too. Please make sure you can attend the full course before signing up. From a pedagogical perspective and out of fairness toward the other participants, you should not miss any part of the course for any reason.

Lecturer: Prof. Dr. Gari Walkowitz (Technical University Bergakademie Freiberg)

Course dates: 18.02. – 20.02.2026 (9 a.m. – 6 p.m.)

Location: Campus Munich (room 0505.EG.514)

This course aims to provide doctoral canidates at the School of Management with a practicalintroduction to conducting field experiments, with a particular focus on field experiments in economics. Rather than focusing on the (econometric) theory of experiments, we will provide students with hands-on advice on how to solve concrete challenges related to planning, conducting, analyzing, and presenting the results of experimental field studies.

Syllabus: Field Experiments: Start to finish

Registration: Please send an email to Prof. Philipp Lergetporer (philipp.lergetporer@tum.de) to register for this course.

Lecturer: Prof. Philipp Lergetporer, PhD (TUM)

Course dates: Each day, the lecture starts at 9 am and ends at 4 pm. There will be a lunch break and coffee breaks. 

Day 1 (06.10.2025): Introduction
Day 2 (07.10.2025): Design basics 
Day 3 (08.10.2025): Planning and management 
Day 4 (09.10.2025): Interpreting treatment effects 
Day 5 (10.10.2025): Practical considerations in field experiments
 

Location: Campus Heilbronn/hybrid

This course introduces doctoral candidates to the principles and methods of game theory, the study of strategic interaction among rational decision-makers. Game theory provides a rigorous framework for analyzing situations where outcomes depend not only on one’s own choices but also on the actions of others. The course focuses on formal modeling of strategic behavior and develops a deep understanding of equilibrium concepts under complete and incomplete information.

This course is open to all doctoral candidates from all management disciplines, as well as doctoral candidates from related disciplines. It is particularly suitable for doctoral candidates with a research topic that draws on formal modeling, but it is equally valuable for anyone seeking a structured way to think about competition, cooperation, signaling, incentives, or negotiation in business settings. Doctoral candidates can be at any stage of their doctorate.

Syllabus: Game Theory

Registration: There is a maximum of 32 doctoral candidates for this course. Please contact Daun Choi (daun.choi@tum.de) to enroll in the course. The application deadline is 30 September.

Lecturer: Prof. Dr. David Wuttke (TUM)

Course dates: The course takes place during the fall/winter semester, 90 minutes per week, for 14 weeks. Each week, the most relevant chapters of “A Course in Game Theory” by M.J. Osborne and A. Rubinstein will be covered, with several hints towards further literature.

Location: Campus Heilbronn (The course takes place 100% in person at the TUM Campus in Heilbronn.)

The course is aimed at doctoral students. The course is designed to equip doctoral students with foundational knowledge and practical insights into the role of generative artificial intelligence (AI) within social science research, with a particular focus on quantitative marketing research. Based on the current literature body, course participants evaluate and discuss current applications related to generative AI in marketing.

Syllabus: Generative AI for Social Science Research

Registration: Please send an email to contact.dm (at) mgt.tum.de to register for this course.

Lecturer: Prof. Dr. Jochen Hartmann (TUM)

Course dates: 

Location: Online

This course will give doctoral students a broad overview of the state-of-the-art technologies and methods (e.g., virtual reality, virtual humans, vocal transformation, AI) used in management research. At the end of this course, participants will be familiar with the different methods and tools available for conducting research, and will develop a concrete research project. We will discuss the benefits of using technologies, and the potential technical difficulties and drawbacks that might be encountered along the way, and how to solve them.

In this course, you will learn about existing methods and develop and work on a concrete research project involving an innovative technology. To this end, students will be asked to develop a final presentation showcasing a research project based on the concepts learned in the course.

Syllabus: Innovative Technologies in Management Research

Registration: By email to anely.bekbergenova@tum.de (Dr. Anely Bekbergenova) before December 22nd. Participants will be admitted on a first come, first served basis.

In your email please include (1) your doctoral research topics, (2) a research question you would like to explore using innovative technology (e.g., virtual reality, artificial intelligence, robotics).

Lecturer: Prof. Dr. Claudia Peus (TUM) and Dr. Anely Bekbergenova (TUM)

Course dates: The course will be held in person at the TUM main campus in Munich (Arcisstr. 21, Building 0505, Room Z577)

Monday, 22.12.2026 – 23:59 – Registration deadline
Tuesday, 24.02.2026 – 9:00 – 17:00 – Presentation by instructor
Wednesday, 25.02.2026 – 9:00 – 17:00 – Presentation by instructor & group/individual work
Wednesday, 04.03.2026 – 9:00 – 17:00 – Presentation by participants & feedback
Wednesday, 18.03.2026 – 23:59 – Research proposal deadline

Location: Z577, Building 0505, TUM Main Campus Munich

This course is designed for doctoral candidates and early-career researchers in operations research, industrial engineering, data analytics, and related fields who are interested in fairness in decision-making, optimization, and constraint programming. A basic understanding of optimization models (linear or integer) and solid programming skills is a prerequisite.

Syllabus: Intelligent Algorithms in Scheduling: Modeling of Fairness, Constraint Propagation

Registration: Please register by 31 January 2026 by contacting office.udsm@mgt.tum.de, as places are limited.

Lecturer: Prof. Dr. Alena Otto (HDSC TUM), Prof. Dr. Olga Battaïa (KEDGE business school)

Preliminary course dates: 03.03, 04.03, 05.03.2026

Location: Campus Heilbronn (L.1.12)

The seminar is designed for doctoral candidates at the TUM School of Management. It provides an indepth examination of literature review methodologies as a key element of academic scholarship. It is especially suited for doctoral candidates planning a literature review for their dissertation and preparing a review paper for publication.

Syllabus: Literature Reviews in Management Research

Registration: Limited spots are available. Priority will be given to doctoral candidates from the TUM School of Management. Application via Email to Prof. Dr. Frank-Martin Belz by 30 September 2025 (23:59 p.m.) with CV and a short motivation letter (max. 1 page), including a tentative topic for a review related to the dissertation.

Lecturer: Prof. Dr. Frank-Martin Belz (TUM)

Course dates: 20.10.2025, 21.10.2025, 22.10.2025, 24.10.2025
 

Location: Campus Munich

The course is intended for doctoral students in Strategic Management, International Management, Innovation, and Entrepreneurship. The goal is to develop ideas and working manuscripts with the aim of later submission for review in top management journals.

Syllabus: Paper and Proposal Development in Strategic Management: Meet the Editors at HEC Paris

Registration: Participation is by application only. Interested scholars should register on Moodle and submit an extended abstract (between 5 and 10 pages of text) of their project proposal or a draft paper to chengguang.li@tum.de by October 6, 2025. Submitted abstracts should describe the project, the intended theoretical contribution, the research design, the empirical approach, and the status of the project idea to date. Please note that submission of an abstract does not guarantee acceptance. The quality of the proposal will be taken into consideration.

Lecturer: Prof. Dr. Joachim Henkel (TUM), Prof. Dr. Chengguang Li (TUM)

Course dates: 

October 13, 1:00 pm – 2:30 pm: Kickoff (Zoom)
October 27: Submissions of proposals / papers
November 3: Reviews due
Week of November 3: Presentations (& receiving reviews) (exact dates and times will be coordinated)
November 24: Revised submissions of proposals / papers
Week of November 24: Second presentations (exact dates and times will be coordinated)
November 30: Submission of final version, to be forwarded to HEC by the instructors
December 1: Proposals / papers are sent to HEC
December 11: Presentations, discussions at HEC

Location: Campus Munich / Campus Heilbronn and / or online / hybrid; HEC Paris

The seminar is designed for doctoral candidates at the TUM School of Management, who intend to employ qualitative research in their dissertation. The ideal time to take this seminar is after the preliminary choice of topic, but before the first entry into the field.

Syllabus: Qualitative Research

Registration: Limited spots are available. Priority will be given to doctoral candidates from the TUM School of Management. Application via Email to Prof. Dr. Frank-Martin Belz by 13 October 2025 (23:59 p.m.) with CV and a short motivation letter (max. 1 page), including a tentative topic for qualitative research.

Lecturer: Prof. Dr. Frank-Martin Belz (TUM)

Course dates: 10.11.2025 - 13.11.2025, 26.11.2025 

Location: Campus Munich

The course is primarily targeted at doctoral candidates interested in, planning to engage in, or currently practicing qualitative research; post-docs and faculty are also welcome to apply. It is not necessary to be a qualitative researcher or have a qualitative research project to participate in this class; a genuine interest in and appreciation of qualitative research of the kind published in, for example, the Academy of Management Journal or Administrative Sciences Quarterly is expected. Thus, participants should either strive toward writing papers for such outlets, or at least hope to understand better the process and methodology by which such papers are produced.

Syllabus: Qualitative Research Methods Workshop

Registration: We are currently planning with up to 20 participants. To apply, please send an email to o.alexy@tum.de, and copy charlotte.cloutier@hec.ca on the email. In this email, please specify (a) area of study or specialization, (b) where you are in your doctoral program, (c) what you currently expect to do once you have finished your doctorate, (d) what kind of qualitative work you are doing or hope to be doing, and (e) what you hope to get out of this class. (f – optional) If you have a qualitative working paper at this point already, feel free to attach it as well. Post-docs and faculty should provide corresponding information.
 

The deadline for applications will be Jan 20, 2026; notifications of acceptance will be communicated by Jan 25, 2026. No feedback on unsuccessful applications can be provided.

Lecturer: Prof. Dr. Charlotte Cloutier (HEC Montreal) & Prof. Dr. Oliver Alexy (TUM)

Course dates: Feb 16, 2026 - Feb 19, 2026

Location: Garching OR downtown Munich (will be announced in due course to applicants)

Doctoral candidates in accounting will be familiarized with recent empirical financial and sustainability accounting research.

Syllabus: Readings Empirical Accounting Research

Registration: Please write an e-mail to carolin.gahr@tum.de by October 8th, 2025 at the latest.

Lecturer: Prof. Dr. Jürgen Ernstberger (TUM)

Course dates: The kick-off meeting is on October 14th, 2025, at 4 p.m. This first session provides an overview of essential criteria for assessing the quality of empirical accounting research papers. In the following sessions, we discuss overview papers, seminal papers on specific topics, or state-of-the-art papers. Doctoral candidates can make suggestions for suitable papers related to their dissertation topics. In the following sessions, we discuss current working papers presented in research seminars and participate in these seminars to learn how to present and discuss a paper.

Location: Campus Munich/hybrid

The course will introduce choice modelling techniques for consumer and marketing analysis. Starting with the theory of consumer choice, the course will discuss different data types available for choice analysis. It will then focus on the specifics of choice experiments, discussing advantages and disadvantages of various experimental designs and data collection procedures. Participants will be familiarized with data handling and analysis considering multinomial logit, random parameters logit, and latent class analysis. To obtain an overview of the literature, participants will present papers from the relevant field.

Syllabus: Applied Choice Analysis

Registration: This course is offered as part of the Promotionskolleg Agrarökonomie. Registration occurs via https://www.agraroekonomik.de/

Lecturer: Prof. Dr. Jutta Roosen (TUM), Dr. Matthias Staudigel (TUM)

Course dates: September 15 – 19, 2025, Mo 13-16, Tu-Th 9-12, 13-16, Fr 9-12

Location: The course is offered in Freising-Weihenstephan. Lecture is in person, attendance required.

This doctoral course aims at giving doctoral students at the School of Management an overview of the essentials quantitative methods. We will not focus on the econometrics, but aim at developing the students’ understanding of what the methods are all about and how they work in practice.

Syllabus: Econometrics I: Research Design and Estimation Methods

Registration: Apply via Email to the instructor until March 20^th 2025.

Lecturer: Prof. Hanna Hottenrott (TUM)

Course dates: 8.4.2025, 9.4.2025 (plus three more days to be announced)

Location: Campus Munich, Seminar room 0505.2566

Knowledge Objectives:

• Doctoral candidates will have a good understanding of academic writing and the structure of the different chapters in a scientific paper.
• Doctoral candidates will know efficient publication strategies

Skills Objectives:

• Doctoral candidates will have improved their skills in research as well as in writing their dissertations or papers.
• Doctoral candidates will have a solid draft of one of their dissertation paper.
• Doctoral candidates will have utilized the processes of revision by positive, constructive peer reviews of the documents by fellow doctoral candidates on their writing and by revising their documents.

Learning Objectives:

• Doctoral candidates will have learned about how to start and finish writing a scientific paper in the context of writing a doctoral thesis.
• Doctoral candidates will have identified standard elements of research papers and used this knowledge to work on their dissertations or papers.

Syllabus: Efficient Academic Writing for Empirical Research

Registration: Please apply until 28th February 2025 to Theresa Treffers, theresa.treffers@tum.de, via email containing:

• Your CV with contact information (email and mobile) and chair of your dissertation
• Dissertation topic (1 sentence)
• Problem statement (3 sentences)
• Data description (3 sentences)
• Target journal (possibly call for papers) and/or conference with deadline

Lecturer: Dr. Fabian Ahrens (TUM), PD Dr. Theresa Treffers (TUM), Prof. Dr. Isabell Welpe (TUM)

Course dates: 14.03.2025, 25.03.2025, 26.03.2025, 01.04.2025, 08.04.2025, 15.04.2025, 22.04.2025, 29.04.2025, 06.05.2025, 13.05.2025, 20.05.2025, 27.05.2025, 03.06.2025, 10.06.2025

Location: Online

South Africa is Africa’s largest economy and probably most developed. It therefore provides an ideal opportunity for various investment forms, including in private equity transactions. Through reading materials, course discussions, and group work, students will gain insight into the fundamental aspects of private equity investing, the perspective of fund managers and investors, those who transact with such funds, and those who regulate the fund industry. The course will provide a unique South African perspective, especially now that South Africa has adopted a Twin Peaks Regulatory Framework, like Australia and the United Kingdom. The course will start with an introduction to the South African regulatory environment in respect of private equity and specifically third-party private equity funds, which represents the dominant organisational form. In addition, the course will discuss private equity in relation to finance, legal practice, tax, types of private equity, fund structuring, latest trends, as well as the key principles of fiduciary law related thereto. At the conclusion of the course it will be clear that South Africa provides sufficient opportunities and protection to international funds which may want to invest in South Africa.

Syllabus: Financial Market Regulation in South Africa as Gateway to Investment in Africa: The Fundamentals of Private Equity Funds and Investing in South Africa

Registration: Please send an email to: sekretariat.jura@tum.de

Lecturer: Prof. Dr. Richard Stevens, LL.M. (University of Stellenbosch)

Course dates: The course will commence on Monday 16 June 2025. It consists of a total of 24 hours of direct class interaction, which will be split over six classes. 

Location: Campus Munich (0503.01.355)

The course is aimed at doctoral students. The course is designed to equip doctoral students with foundational knowledge and practical insights into the role of generative artificial intelligence (AI) within social science research, with a particular focus on quantitative marketing research. Based on the current literature body, course participants evaluate and discuss current applications related to generative AI in marketing.

Syllabus: Generative AI for Social Science Research

Registration: Please send an email to contact.dm@mgt.tum.de to register for this course.

Lecturer: Prof. Dr. Jochen Hartmann (TUM)

Course dates:
09/05/2025 09:30 – 13:30 Kick-off
22/05/2025 15:30 – 17:30 GenAI Lab Seminar Series
26/06/2025 15:30 – 17:30 GenAI Lab Seminar Series
10/07/2025 12:30 – 15:30 Paper Discussion
10/07/2025 15:30 – 17:30 GenAI Lab Seminar Series
18/07/2025 09:30 – 17:30 Presentation of Research Ideas

Location: Online (MS Teams)

This course will give doctoral students a broad overview of state-of-the-art technologies and methods (e.g., virtual reality, virtual humans, vocal transformation, AI) used in management research, specifically in leadership and social science related research. At the end of this course, participants will be familiar with the different methods and tools available for conducting research in management and will develop a concrete research project. We will discuss the benefits of using technologies, and the potential technical difficulties and drawbacks that might be encountered along the way, and how to solve them.

In this course, you will learn about existing methods and develop and work on a concrete research project involving an innovative technology. To this end, students will be asked to develop a final presentation showcasing a research project based on the concepts learned in the course.

Syllabus: Innovative Technologies in Management Research

Registration: By email to anely.bekbergenova@tum.de (Dr. Anely Bekbergenova) before August 11th. Participants will be admitted on a first come, first served basis.

Lecturer: Dr. Anely Bekbergenova and Prof. Dr. Claudia Peus (TUM)

Course dates:
Monday, 11.08.2025 – 23:59 – Registration deadline
Tuesday, 09.09.2025 – 9:00 – 17:00 – Presentation by instructor
Wednesday, 10.09.2025 – 9:00 – 17:00 – Presentation by instructor & group/individual work
Tuesday, 16.09.2025 – 9:00 – 17:00 – Presentation by participants & feedback
Tuesday, 23.09.2025 – 23:59 – Research proposal deadline

Location: The course will be held in person at the TUM main campus in Munich (Arcisstr. 21, Building 0505, Room Z577)

This course is for researchers on the doctoral or post-doctoral levels who are beginners in economic laboratory experiments. It will enable you to

• decide whether a laboratory experiment is appropriate to address some research question;
• find research questions in your area of interest that a laboratory experiment can address;
• develop an experimental design to address such a research question.

In addition, the course will offer you hands-on training on how to bring experiments to the laboratory. It will cover common practical issues, such as which software to use, how to recruit participants, or how to conduct an experiment.

The course will be most beneficial for you if you plan to run your own experiment soon. It will be particularly helpful for you if you consider using experimenTUM, TUM’s laboratory for experimental research in economics.

Syllabus: Introduction to Experimental Economics

Registration: Write to Andreas Ostermaier no later than July 25, 2025 to sign up (andreas.ostermaier@zu.de). Please state your primary research area and method. Mention also what motivates you to sign up for this course and whether you are planning to run an experiment.

If you have a research question or idea for an experiment that you would like to see as an assignment, remember to include a very brief proposal in your application. If you have any introductory readings, feel free to suggest these, too.

Please make sure you can attend the full course before signing up. From a pedagogical angle and out of fairness toward the other participants, you should not miss any part of the course for any reason, including the supervision of student exams.

Lecturer: Prof. Dr. Andreas Ostermaier (Zeppelin University)

Course dates: The course will extend over three days. The tentative schedule is as follows.

First day:

• Visit to experimenTUM (interactive);
• The ultimatum game (lecture & discussion);
• Standard designs (first assignment, interactive).

Second day:

• Standard designs cont’d (first assignment, interactive);
• Common design choices (lecture & discussion);
• Designing your own experiment (second assignment, interactive).

Third day:

• Designing your own experiment, cont’d (second assignment, interactive);
• Benefits and pitfalls of lab experiments (lecture & discussion);
• Discussion of participants’ projects.

The course will typically at 9 a.m. in the morning and end no later than 5 p.m. in the afternoon. The afternoons of the first and second days are partly reserved for working on your assignments. I will always be around to assist you, commuting between the teams.

Location: Campus Munich

The course is aimed at doctoral candidates with an interest in Econometric Methods. The goal of this course is to introduce the concepts of Influence Functions, Local Robustness/Neyman Orthogonality and Machine Learning. The course will combine both general theory and applications (among others) to

• Two step estimation procedures
• Machine Learning Causal Inference
• Inequality of Opportunity
• Empirical Welfare Maximization

After the course students should be more familiar with how to use Machine Learning in economic applications with desired statistical guarantees.

Syllabus: Local Robustness and Machine Learning

Registration: Until 10th of June by email.

Lecturer: Joël Terschuur

Course dates: June 16-20, 2025 from 10:00-12:00 and 14:00-17:00

Location: Campus Munich