PhD Position: Deep Learning for Medical and Scientific Imaging
The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5–2 years, total 3–4 years) on deep learning for medical imaging.
This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging.
The Professorship for Machine Learning at TUM works on machine learning, artificial intelligence, and information processing, with a current focus on foundation models, data-centric research, and applications to scientific and medical imaging.
Your Profile:
- Master's degree in Computer Science, Electrical and Computer Engineering, Mathematics, Physics, or a related field
- Strong background in mathematical and computational sciences
- Knowledge in machine learning or medical imaging is a plus
- Driven, with a strong work ethic and desire to do high-quality research
TUM is consistently ranked among the best universities in Europe and as the top technical university in Germany. Our group is part of the School of Computation, Information and Technology, and we collaborate internationally with groups in the US and Europe.
We Offer:
- A fully funded position (TV-L E13 100%)
- An engaging research project in a diverse, collaborative team
- The opportunity to earn a PhD at a world-class institution
Please send your application to [email protected]. Include:
- A brief letter explaining your interest in this project and our work
- Your CV
- Transcripts of grades
We value thoughtful applications over formality, but will not consider incomplete or generic submissions.
The position is open until filled. We plan to fill the position as soon as possible, and review of applications starting mid-February.
Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. Schwerbehinderte Bewerberinnen und Bewerber werden bei ansonsten im wesentlichen gleicher Eignung, Befähigung und fachlicher Leistung bevorzugt eingestellt.
Kontakt: [email protected]