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Ludwig-Maximilians-Universität München

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PhD Positions in Reliable Artificial Intelligence at Ludwig-Maximilians-Universität München and Technical University of Munich Ludwig-Maximilians-Universität München in Germany

PhD Positions in Reliable Artificial Intelligence at Ludwig-Maximilians-Universität München and Technical University of Munich

Ludwig-Maximilians-Universität München

Germany

Jan 13, 2026

Keywords

Computer Science
Electrical Engineering
Mathematics
Medical Science
Robotics
Statistics
Machinelearning
Secure Ai
Engineering
Responsibleai
Ai In Healthcare
Reliable Artificial Intelligence
Decision-making Algorithms

Description

The relAI Konrad Zuse School of Excellence in Reliable AI is now accepting applications for its 2025 PhD program, jointly organized by Ludwig-Maximilians-Universität München (LMU) and Technical University of Munich (TUM), and funded by the DAAD German Academic Exchange Service. This interdisciplinary program focuses on reliable artificial intelligence, offering research opportunities at the intersection of machine learning, mathematics, engineering, and the responsible development of AI systems. Research areas include AI safety, security, privacy, responsibility, algorithmic decision-making, robotics, healthcare, and mathematical foundations. PhD candidates benefit from a vibrant academic environment, professional development, and close collaboration with leading researchers at both LMU and TUM. Each doctoral researcher is supervised by a relAI Fellow and enrolls at either LMU or TUM, depending on the supervisor's affiliation. Notable relAI Fellows and spokespersons include Prof. Gitta Kutyniok (LMU Director of relAI School), Prof. Frauke Kreuter, Prof. Eyke Hüllermeier, Prof. David Rügamer, Prof. Albrecht Schmidt, Prof. Hinrich Schuetze, Prof. Volker Tresp, Prof. Solveig Vieluf, and Prof. Stephan Günnemann. Funded positions offer a full three-year salary (TV-L E13) and additional support such as travel grants for conferences or research stays. Applicants must hold an excellent master's degree (or equivalent) in computer science, mathematics, engineering, natural sciences, or other data science/machine learning/AI-related disciplines. A strong interest in reliable AI, including safety, security, privacy, or responsibility, is required. Proof of English proficiency at level C1 or equivalent (or proof that the degree was taught mainly in English) is mandatory. Application materials include a CV, diplomas, transcripts, a short motivation statement, choice of two preferred relAI Fellows as supervisors with explanations, names and addresses of two referees for recommendation letters, and proof of English proficiency. The application deadline is January 13, 2026. For more information and to apply, visit the official relAI application portal.

Funding

Funded PhD positions include a full three-year salary (TV-L E13) and additional support such as travel grants for conferences or research stays. The program is funded by the DAAD German Academic Exchange Service. Each PhD researcher is enrolled at LMU or TUM, depending on the supervisor's affiliation.

How to apply

Prepare your CV, diplomas, transcripts, motivation statement, and proof of English proficiency. Select two preferred relAI Fellows as supervisors and explain your choices. Submit your application via the provided online portal before January 13, 2026.

Requirements

Applicants must hold an excellent master's degree (or equivalent) in computer science, mathematics, engineering, natural sciences, or other data science/machine learning/AI-related disciplines. They should demonstrate a strong interest in reliable AI, including aspects such as safety, security, privacy, or responsibility, and provide proof of English proficiency at level C1 or equivalent (or proof that their university degree was taught mainly in English). Required application materials include a CV, copies of diplomas and transcripts, a short motivation statement, choice of two preferred supervisors (relAI Fellows) with explanations, names and addresses of two referees for recommendation letters, and proof of English proficiency.

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