Technical University of Munich
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PhD Position in Representation Learning and Next-Generation Medical Foundation Models Technical University of Munich in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position at TV-L E13, 100% full-time, fixed-term for 3+ years. The project is described as well-funded and includes dedicated funding for international conference participation.
Country
Germany
University
Technical University of Munich
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About this position
PhD position at the Technical University of Munich (TUM) in Munich, Germany, focused on representation learning, self-supervised learning, medical deep learning, foundation models, and multimodal learning.
The project is hosted by the AI-Assisted Healthcare Lab at the TUM School of Medicine and Health and is embedded in TUM University Hospital. The research aims to study why self-supervised objectives and predictive architectures may bias representations toward frequent patterns, and how to correct this bias so rare but clinically important findings are preserved.
Research directions include information geometry of learned representations, multimodal representation learning and information decomposition, learning with missing/partial/irregular observations, temporal and longitudinal representation learning, and new learning objectives and architectures for medical foundation models.
This is a fully funded PhD position (TV-L E13, 100%, fixed-term for 3+ years). The environment includes access to large-scale multimodal clinical datasets, high-end GPU and storage infrastructure, international collaborations, and funding for conference participation.
Applicants should have a strong BSc and MSc in computer science, machine learning, mathematics, physics, engineering, or a related field. Strong ML/deep learning foundations, PyTorch or similar experience, strong programming and analytical skills, and excellent English are expected.
To apply, send a CV, cover letter, and complete Bachelor’s and Master’s transcripts by email to the listed supervisors. No formal deadline is stated in the post.
Funding details
Fully funded PhD position at TV-L E13, 100% full-time, fixed-term for 3+ years. The project is described as well-funded and includes dedicated funding for international conference participation.
What's required
Applicants should have a strong BSc and MSc degree in computer science, machine learning, mathematics, physics, engineering, or a related field. Strong foundations in machine learning and deep learning are required, along with experience with PyTorch or similar frameworks. Interest in representation learning, self-supervised learning, multimodal learning, foundation models, or ML theory is expected. Strong programming and analytical skills and excellent written and spoken English are required.
How to apply
Prepare a CV, cover letter, and complete academic transcripts for Bachelor’s and Master’s degrees. Send the application by email directly to the listed contacts. Use the provided TUM email addresses for Cosmin Bercea, Keno Bressem, and Lisa Adams.
More information can be found here
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