The Machine Learning Group consistently seeks exceptional individuals for the following positions:
- Postdoctoral Researcher
- Ph.D. Researcher
- Student Assistant
How to apply
We welcome applications for the positions described below and unsolicited inquiries.
In the latter case, candidates can email their applications to sekr(∂)ml.tu-berlin.de, ensuring to include a cover letter, CV/resume, and academic transcripts.
What we are offering
The Machine Learning Group is located in the center of Berlin, close to the public park Tiergarten that spans 2.1 square kilometers.
Berlin is one of Germany’s centers for machine learning research and industry, housing three universities and more than 70 research institutes such as the Fraunhofer and Max Planck societies.
Prof. Klaus-Robert Müller, one of Germany’s most-cited scientists, leads the Machine Learning Group while also serving as director
of BIFOLD.
The Machine Learning Group operates under the umbrella of BIFOLD, which benefits from 22 million EUR in permanent annual funding from the federal state.
We are offering:
- Paid annual leave: 30 days plus December 24th/31st and ten public holidays (Ph.D. and postdoc)
- Flexible working hours and the possibility to (partially) work from home
- Family-friendly environment, e.g., childcare services and the possibility of a leave to care for relatives (Ph.D. and postdoc)
- A cheap public transport ticket (a subsidized version of the Deutschlandticket) for all public transport in Germany, including regional trains (Ph.D. and postdoc)
- Salary according to Germany’s collective agreement TV-L for federal-level public service employees (Ph.D. and postdoc)
- A friendly and welcoming culture
- A group of more than 50 motivated researchers with diverse interests and backgrounds
- A wide network of academic and industry collaborators
- Many small offices with modern equipment
- A large-scale compute cluster with many state-of-the-art GPUs, such as A100
- An annual multi-day retreat and optional team events like canoeing
Our diverse team brings together expertise from disciplines such as physics, chemistry, and computer science.
Applicants are encouraged to explore our research profile and team members to identify areas of common interest.
They may also browse our secondary team overview which allows filtering team members by research interests more granularly.
What we are looking for
We seek applications from individuals with a proven track record of excellence in their respective fields.
Proficiency in German is not a mandatory requirement unless explicitly mentioned.
Our group firmly believes in inclusion, and we invite applications from candidates of all genders, ages, and ethnic backgrounds. We prioritize applications from qualified individuals with disabilities.
Our Team Roles
Postdoctoral Researcher
Applicants should be prepared to provide letters of recommendation upon request.
Eligibility criteria include:
- Ph.D. in a related field such as computer science or physics
- Demonstrated track record of first-author publications in peer-reviewed venues (e.g., NeurIPS, ICML, ICLR, CVPR, JMLR, TMLR, PAMI, IEEE journals, or domain-specific conferences/journals) or equivalent projects
- Proven programming experience, e.g., in languages such as Python, C++, Rust, or Julia, frameworks such as PyTorch, TensorFlow, NumPy, or JAX, and version control systems like Git
- Excellent communication skills and ability to collaborate
- Proficiency in English
- Contributions to open-source software are desirable but not required
- Interdisciplinary and cooperative project experience is desired but not required
Ph.D. Researcher
We fill some of our Ph.D. positions via the BIFOLD graduate school, which admits new candidates every year.
Applicants should be prepared to provide letters of recommendation upon request.
Eligibility criteria include:
- Master’s degree in a related field such as computer science or physics
- Proven programming experience, e.g., in languages such as Python, C++, Rust, or Julia, frameworks such as PyTorch, TensorFlow, NumPy, or JAX, and version control systems like Git
- Excellent communication skills and ability to collaborate
- Proficiency in English
- Publications in peer-reviewed venues are desirable but not required
- Contributions to open-source software are desirable but not required
Student Assistant
We seek students to join our team for 40, 60, or 80 monthly working hours for teaching and/or research roles.
Expected qualifications:
Required criteria:
- Proficiency in English (German not required)
- Effective communication skills and ability to collaborate.
- Residence in or near Berlin (partial remote work possible).
- Enrollment at a German university
- Minimum commitment of one semester
Desirable Skills (role-dependent):
- Familiarity with machine learning, e.g., from our courses or equivalent
- Proficiency in programming with Python or a similarly abstract or lower-level language (e.g., Julia, C++, Rust), e.g., as acquired from our courses or equivalent
- Experience developing machine learning-related code using frameworks like PyTorch, JAX, or similar.
- If interested in direct student contact: pedagogical skills, such as effective communication, empathy, and presentation abilities.
We welcome students with diverse skills and interests to apply!
You don’t need to be an expert in all the mentioned areas, but having a solid foundation in some key skills will be beneficial.
Please additionally review:
Application details:
Please submit your application in English to sekr(∂)ml.tu-berlin.de, including the following PDF documents:
- Cover letter (1 page maximum):
- Describe your incentives, e.g.:
- Do you want to contribute to research and/or teaching and/or another area?
- Do you want to work in a specific subfield, e.g., in research or teaching?
- Not having a specific preference is not a problem! But disclosing an existing preference can help us channel your application.
- Describe how you could create value for our group (how your skills would align with our incentives).
- Avoid generic phrases commonly suggested by AI language models. It is OK to write less than one page (quantity is not important).
- Tip: Review resources on effective cover letter writing, such as this HBR article.
- CV/resume (1 page maximum):
- Focus on qualifications relevant to the role. It is OK (and often helpful) to not mention qualifications irrelevant to the role.
- Include links to public projects (e.g., GitHub repositories or papers) if you have any.
- Tip: Consult resources on resume writing, like this HBR guide.
- Additional documents: Consolidate supplementary materials (e.g., academic transcripts) into a single PDF.