Master and Bachelor Thesis Supervision (public landing page)

Finding a supervisor

Interested students can contact us with the thesis application form, a curriculum vitae/resume, and an optional cover letter. Please provide evidence for relevant skills. Applicants require significant experience in machine learning, e.g., as acquired through the courses offered by our group (passed with the grade “good” or better) or some equivalent. This typically includes a deep conceptual understanding of machine learning and profound programming experience. The necessary skills may vary depending on the topic, e.g., purely theoretical theses require more mathematical than programming skills, albeit such theses are the exception and not the norm. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest their own topics/ideas. Either way, candidates should have sufficient abilities before applying because it will otherwise be impossible to independently and successfully finish the thesis within a reasonable timeframe given a thesis equivalent to the norm.

Our group supervises as many students as possible, but we typically do not have the capacity to supervise every interested student. We are a large group and use the formalized application procedure to determine our supervisory capacity given the student's research interests and profile.

You can contact our group via two complementary channels:

1. Top-down: Write a mail to jobs@ml.tu-berlin.de. We will match you with suitable internally advertised topics (if existent). Furthermore, we will give you the contact information of researchers in our group if they are compatible with your profile and have free capacity. Please briefly mention the names of the researchers you have contacted unsolicitedly (if any).

2. Unsolicited: You can contact the researchers in our groups directly. We provide an overview list below and will soon update our main website. Unsolicited applications can be particularly helpful for students with prior experience in a specific research area and clear preferences. To guide this form of the application process, you may skim our group’s publications in your area of interest and contact the respective authors, for example.

Researchers:

• ML for Quantum Chemistry: Stefan Chmiela (stefan@chmiela.com)

• Kernel Learning: Stefan Chmiela (stefan@chmiela.com)

• Explainable AI: Grégoire Montavon (gregoire.montavon@tu-berlin.de)

• Anomaly detection/Unsupervised learning: Robert Vandermeulen (vandermeulen@tu-berlin.de)

• Probabilistic modeling and inference: Shinichi Nakajima (nakajima@tu-berlin.de)

• Time series analysis and signal processing: Andreas Ziehe (andreas.ziehe@tu-berlin.de)

• Multimodal signal acquisition and analysis - neurotechnology + biomedical sensing: Alexander von Lühmann (vonluehmann@tu-berlin.de)

• ML for Security & Privacy: Daniel Arp (d.arp@tu-berlin.de)

• Explainable AI & Natural Language Processing: Oliver Eberle (oliver.eberle@tu-berlin.de)

• Learning with multiple modalities: Jannik Wolff (jannik.wolff@tu-berlin.de)

• Robustness against spurious correlations in DNNs, neural cellular automata: Lorenz Linhardt (l.linhardt@campus.tu-berlin.de)

• Combining Explainable AI & Deep Generative Models like Diffusion Models, Large Language Models, GANs, VAEs and Normalizing Flows in different modalities: Sidney Bender (s.bender@tu-berlin.de)

• ML for Quantum Chemistry: Jonas Lederer (jonas.lederer@tu-berlin.de)

You can find further information about our group members on our group's publication list and the BIFOLD website.

Publicly advertised open topics

We advertise most topics internally (link to our internal wiki) because they are related to ongoing and unpublished research.

After having found a supervisor

Students prepare a proposal that includes

• the research question and its context/motivation,
• related work,
• preliminary methodological and/or experimental results,
• and formalities such as the number of ECTS credits and the writing time as specified in the student’s examination regulations.

The supervisor can help the student with writing the proposal. Students can register the thesis with the examination office after Prof. Müller approves the proposal. We encourage students not to underestimate the time required for writing the proposal. Furthermore, consider that we may require some time to review the proposal. Therefore, it is helpful to contact our group early.