PhD Position in Deep Learning for Medical Imaging at Technical University of Munich Technical University of Munich in Germany

The Professorship of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), invites applications for a fully funded PhD position focused on deep learning for medical imaging. This opportunity is part of a DFG-funded project dedicated to developing advanced deep learning methods for both medical and scientific imaging. The research group specializes in machine learning, artificial intelligence, and information processing, with current interests in foundation models, data-centric research, and impactful applications in scientific and medical imaging.

TUM is recognized as one of Europe’s leading universities and the top technical university in Germany, offering an outstanding environment for research and academic growth. The successful candidate will join the School of Computation, Information and Technology, collaborating with international partners in the US and Europe. The position provides a stimulating research project within a diverse and collaborative team, and the opportunity to earn a PhD at a world-class institution.

Applicants should possess a Master’s degree in Computer Science, Electrical and Computer Engineering, Mathematics, Physics, or a closely related field. A strong foundation in mathematical and computational sciences is essential, and prior knowledge in machine learning or medical imaging is advantageous. The group values motivated individuals with a strong work ethic and a commitment to high-quality research. The position is suitable for disabled persons, who will be given preference in case of generally equivalent suitability, aptitude, and professional performance.

The position is fully funded (TV-L E13 100%) for an initial contract of 1.5–2 years, with a total duration of 3–4 years. The application process requires submission of a brief letter explaining your interest in the project and the group’s work, a CV, and transcripts of grades. Thoughtful applications are preferred over formal ones, and incomplete or generic submissions will not be considered. Applications are reviewed starting mid-February, and the position will remain open until filled. For further details and to apply, visit the official application link or contact the provided email address.

Data protection information in accordance with GDPR is available and must be acknowledged upon application. TUM is committed to diversity and inclusion, and encourages applications from all qualified candidates.