Technische Universität Berlin, through the Berlin Institute for the Foundations of Learning and Data (BIFOLD), is offering 10 PhD positions as research assistants in the fields of Data Management and Machine Learning. These positions are part of the BIFOLD Graduate School, a leading center for artificial intelligence research in Germany. The research focus includes Data Management, Machine Learning, and their intersection, covering topics such as big data systems, distributed analysis, database systems, data integration, data science pipelines, and advanced machine learning algorithms. Research groups are led by renowned professors including Volker Markl, Klaus-Robert Müller, and others, with opportunities to work on cutting-edge projects in AI, quantum chemistry, and earth observation.

Applicants should hold a Master's degree (or equivalent) in computer science or a closely related field, with strong academic records and relevant experience in programming and research. Specific requirements depend on the chosen research area: Data Management applicants should have hands-on experience with big data or database systems, while Machine Learning applicants need strong theoretical and practical ML skills. Interdisciplinary candidates should demonstrate experience in applied ML and data science pipelines. Excellent English communication skills are required, and basic German or willingness to learn is expected. Early research and teaching experience are advantageous.

The positions are fully funded, salaried according to TV-L 13 Berliner Hochschulen, and include comprehensive mentoring, funding for conference participation, and access to international academic events. The working environment is international, collegial, and family-friendly. Applications from women, individuals with disabilities, and candidates of all nationalities are encouraged to ensure diversity and equal opportunity.

To apply, candidates must submit a single PDF including the application form, motivation letter, CV, academic transcripts, certificates, and publication list by email to [email protected], quoting the reference number IV-531/25. The application deadline is February 13, 2026. For more information, applicants should review the research groups and thesis opportunities on the BIFOLD website.