10.06.2026

Interne Jobbörse der TU München:
Wissenschaftlicher Mitarbeiter (Doktorand)(m/w/d)   

Über uns… Die Technische Universität München zählt zu den besten Universitäten Europas. Spitzenleistungen in Forschung und Lehre, Interdisziplinarität und Talentförderung zeichnen sie aus. Dazu kommen starke Allianzen mit Unternehmen und mit wissenschaftlichen Einrichtungen auf der ganzen Welt. Die TUM ist eine der drei ersten Exzellenz-Universitäten Deutschlands. Die TUM School of Management ist darüber hinaus die erste Management School an einer technischen Universität in Deutschland, die die Triple Crown Akkreditierung erhalten hat. Weltweit können nur etwa 80 Institutionen (etwa 1 %) diese Auszeichnung aufweisen. Wir bieten… • ein attraktives und kollegiales Arbeitsumfeld, • eine intellektuell herausfordernde und inhaltlich spannende Tätigkeit, • die Möglichkeit, Expertise im strategischen Management für eine Karriere in der Wissenschaft und in der Wirtschaft aufzubauen. Wir suchen… • überdurchschnittliche Lernbereitschaft, Ehrgeiz und Teamfähigkeit, • überdurchschnittlicher Master-Abschluss in BWL, VWL oder verwandten Fachrichtungen, • (Sehr) gute Englischkenntnisse in Wort und Schrift. • Praxiserfahrung ist hilfreich, aber nicht notwendig.
Mehr

Kontakt: chengguang.li@tum.de

10.06.2026

Wissenschaftliches Personal :
PhD Position in Trustworthy AI for Medicine at the Chair for AI in Medicine   

The Institute for AI and Informatics in Medicine at TUM University Hospital is looking for a PhD candidate to work on research at the intersection of machine learning, data privacy, and medical imaging. The position is part of a three-year research project that investigates how privacy-preserving methods for medical AI can be made more rigorous, starting from systematically evaluating existing approaches to developing new methods with formal guarantees.

About us

The position is supervised by Dr. Alexander Ziller within the Institute for AI and Informatics in Medicine, directed by Prof. Daniel Rueckert, at the Technical University of Munich. The research group is based at TUM University Hospital (Klinikum rechts der Isar) and works on privacy-preserving machine learning for healthcare, with a focus on bridging the gap between empirical privacy methods and provably robust solutions. As part of a small, focused research team, you will have significant ownership over your research direction and work in close collaboration with the PI.

Your role

You will conduct research across three interconnected areas: benchmarking existing privacy-preserving methods in medical AI under standardized conditions, investigating realistic vulnerabilities in deployed AI systems, and developing novel approaches to generating synthetic medical data with formal privacy guarantees. The work involves both theoretical analysis and large-scale experimentation on medical imaging data. Results will be published at leading machine learning, medical imaging, and security venues and journals.

Your profile

• Master's degree in computer science, physics, electrical engineering, mathematics, or a related quantitative discipline
• Solid mathematical foundations and strong programming skills
• Proficiency in Python and experience with deep learning frameworks such as PyTorch
• Ability to work independently and take initiative in driving research forward
• Interest in topics such as data privacy, generative models, or medical image analysis
• Fluency in written and spoken English
• Prior research or professional experience is a plus

What we offer

• Full-time position (100%, TV-L E13) for three years with the opportunity to pursue a doctoral degree
• A research environment with access to high-performance computing infrastructure and medical datasets
• Publication-oriented research targeting top-tier conferences and journals
• An interdisciplinary environment at one of Europe's leading technical universities and university hospitals

The position is available from October 2026. A later start date can be arranged if needed.

How to apply

Please send your application as a single PDF to alex.ziller@tum.de with the subject line [PhD Application] <Your Name>. Include:

• Your CV
• University transcripts (Bachelor and Master)
• A brief description (max half a page) of a technical project (from research, your thesis, or work) that you are proud of. What made it challenging?
• Optionally, a link to a code repository, thesis, or publication you contributed to

Applications are reviewed on a rolling basis. Early submission is encouraged. The deadline for full consideration is July 5, 2026.

Our process

We believe in a transparent hiring process. Shortlisted candidates will first be invited to a brief introductory conversation (~30 minutes). Those who advance will be invited for an on-site visit including a longer discussion and the chance to meet the team. We aim to inform all applicants of their status by the end of July.

If you have questions about the position, feel free to reach out at the email above.

TUM is committed to increasing the proportion of women in research and explicitly encourages applications from qualified women. Applications from candidates with disabilities who are equally qualified will be given preference.