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Thomas Bayes Fellows

The Thomas Bayes Fellowship attracts outstanding postdoctoral researchers and early-career academics to MCML, offering the opportunity to pursue independent research in machine learning and AI.

Fellows benefit from high visibility, strong mentoring, and full integration into Munich’s leading machine learning ecosystem for a fixed term of two to three years.

Thomas Dagès

Dr.

→ Group Daniel Cremers
Computer Vision & Artificial Intelligence

Thomas Dagès

is a Thomas Bayes postdoctoral Fellow of MCML at the Chair of Computer Vision and Artificial Intelligence at TU Munich.

His research interests include regular and geometric computer vision, signal and image processing, analysis, and synthesis, and deep learning interpretability.

Fabian Fumagalli

Prof. Dr.

Thomas Bayes Fellow

Statistical Learning and Data Science

Fabian Fumagalli

is a Thomas Bayes Fellow of MCML and Interim Professor at the Chair of Statistical Learning and Data Science at LMU Munich.

His research advances explainable AI (XAI) by developing theoretically grounded, efficient methods for interpreting machine learning models, focusing on scalable and reliable Shapley-based explanations.

Research Group @MCML

Raphael Rehms

Dr.

→ Group Daniel Rückert
Artificial Intelligence in Healthcare and Medicine

Raphael Rehms

is a Thomas Bayes postdoctoral Fellow of MCML at the Chair for AI in Healthcare and Medicine at TU Munich.

His current research centers on fusing probabilistic and Bayesian approaches with deep learning to address critical challenges in the medical domain. Key areas of his work involve enhancing model reliability through meaningful uncertainty quantification and the effective utilization of prior information. Besides methodological advances, he is also interested in real-world applications where data exhibit problems like sparsity, measurement issues, and challenges with respect to privacy.