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| Interested students can contact a group member listed below with a curriculum vitae/resume and an optional cover letter. Among other relevant aspects, these documents should inform about the grades in relevant modules. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Students should also describe their research interests/ideas, including '''suitable and proven''' skills, e.g., in Python programming, mathematics, or scientific writing. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest own topics/ideas. | Interested students can contact us with the [[https://1drv.ms/w/s!Ahi16FpIWOkRkttoYVVe5krTQqpBTg?e=XRslcE|thesis application form]], a curriculum vitae/resume, and an optional cover letter. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest own topics/ideas. |
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| '''Though our group tries to supervise as many students as possible, we typically do not have the capacity to supervise every interested student.''' | There are two complementary ways to find a supervisor: 1. Top-down: write a mail to teachingteam@ml.tu-berlin.de. We will match you with suitable internally advertised topics if possible. 2. Unsolicited: you can contact the researchers below if they work in a research area that suits you. |
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| Our senior researchers can identify suitable internally advertised topics or suggest suitable supervisors within our group: | '''Our group tries to supervise as many students as possible, but we typically do not have the capacity to supervise every interested student.''' Researchers: |
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Alternatively, it is possible to contact junior researchers directly with '''specific''' ideas or inquiries: |
* Explainable AI & Natural Language Processing: Oliver Eberle (oliver.eberle@tu-berlin.de) |
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| * 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) |
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| You can find further information about our group members, including their research, on [[https://doc.ml.tu-berlin.de/publications/|our group's publication list]] and the [[https://www.bifold.berlin/research/people|BIFOLD website]]. | You can find further information about our group members on [[https://doc.ml.tu-berlin.de/publications/|our group's publication list]] and the [[https://www.bifold.berlin/research/people|BIFOLD website]]. |
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| === Open topics === Group members can find open topics in our [[https://wiki.ml.tu-berlin.de/wiki/IDA/ThesisTopicsNewInternal|internal wiki]]. |
=== Publicly advertised open topics === We advertise most topics internally ([[https://wiki.ml.tu-berlin.de/wiki/IDA/ThesisTopicsNewInternal|link to our internal wiki]]) because they are related to ongoing and unpublished research. |
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| == General procedure == After the supervisor has been found, the student must write a proposal that includes the research question and its context/motivation, related work, '''preliminary''' methodological and/or experimental results, and formalities such as time constraints. After Prof. Müller approves the proposal, the student can register the thesis, and the writing period begins (typically three months for BA and six months for MA). This implies that a thesis often requires more time than the official writing period. It might be possible to hand in the thesis early (check with the respective examination regulations for the student’s degree program). However, we encourage significant time between registration and submission. |
|| '''MS/BS''' || '''Topic''' || '''Supervisor + email address''' || '''Date of entry''' || '''Additional information'' || || - || - || -|| - || - || == 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. |
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. Prior knowledge of machine learning is mandatory, e.g., as acquired through the courses offered by our group. Many theses are connected to ongoing research in our group. However, it is also possible for students to suggest own topics/ideas.
There are two complementary ways to find a supervisor:
Top-down: write a mail to teachingteam@ml.tu-berlin.de. We will match you with suitable internally advertised topics if possible.
- Unsolicited: you can contact the researchers below if they work in a research area that suits you.
Our group tries to supervise as many students as possible, but we typically do not have the capacity to supervise every interested student.
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 (wolff [dot] jannik [at] icloud.com)
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.
MS/BS |
Topic |
Supervisor + email address |
Date of entry |
Additional information |
- |
- |
- |
- |
- |
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.