We are looking for committed and reliable students to support our scientists in their research activities. Please see our website https://web.ml.tu-berlin.de/student-assistants for detailed information on research focus areas and projects that Student Assistants are currently needed for.

Tasks to be carried out under guided supervision:

approx. 40 % - Code implementation of existing and novel machine learning models (such as data preparation and model training on GPU cluster) as well as documentation and presentation of code and experimental results to supervising scientists
approx. 20 % - Analysis and visualization of data
approx. 10 % - Reviewing existing machine learning literature
approx. 10 % - Provide input on conceptual ideas and assist in writing scientific papers
max. 20 % - Other support for the department: Assistance in fulfilling teaching obligations by helping to prepare our courses (e.g. “Machine Learning”, “Cognitive Algorithms” or “Python for Machine Learning”) as well as support with exams and the preparation of exercise and course materials

Must:

• Excellent proficiency in programming with Python or a similarly abstract or lower-level language (e.g., Julia, C++, Rust)
• In-depth and extensive knowledge of machine learning
• Very good written and spoken English skills
• Enrollment at a German university

Can:

• Experience in developing machine learning-related code using frameworks like PyTorch, JAX, or similar
• Ability to engage with research papers
• Effective communication and strong collaboration skills
• Good written and spoken German

Party responsible for specialist area / point of contact for job posting: Y. Mai-Linde
Period of employment: limited for 2 years
Apply to: sekr@ml.tu-berlin.de

Please submit your written application including cover letter, your CV, certificate of enrollment, and where applicable, current transcript of records, with the reference number to the place of employment indicated above.
In the interest of promoting equality opportunities for men and women, applications from women with suitable qualifications are particularly encouraged.