Technical University of Munich
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Ph.D. Position in Causality and Representation Learning Technical University of Munich in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded Ph.D. position at 100% TV-L E13; generous funding for travel and research visits is available.
Deadline
Jan 31, 2027
Country
Germany
University
Technical University of Munich
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About this position
This Ph.D. project is hosted by the Emmy Noether research group “Causality and Representation Learning for Interventional Foundation Models” at the Professorship of AI for Scientific Modeling on the Garching Campus of the Technical University of Munich.
The position sits at the interface of machine learning and statistics, with a focus on questions in causality and representation learning. The successful candidate will contribute to research on robust methods for causal representation learning and on the theoretical understanding of latent structure that emerges in unsupervised representation learning. The advert highlights an interdisciplinary and international environment at TUM, along with support for travel and research visits.
The work is led by Simon Buchholz, who heads the research group. The setting is especially suitable for students interested in rigorous, theory-driven work with a computational component, and in developing methods and analyses relevant to interventional foundation models.
Funding details
Fully funded Ph.D. position at 100% TV-L E13; generous funding for travel and research visits is available.
What's required
Applicants must hold a Master’s degree in mathematics, statistics, computer science, machine learning, or a related field by the start date; candidates currently completing their Master’s degree may apply. The group seeks candidates with an interest in causality and either a strong mathematical background, particularly in probability theory and statistics, or strong computational and programming skills, especially in Python and PyTorch. A combination of mathematical/statistical and computational expertise is especially welcome. Knowledge of German is not required.
How to apply
Apply preferably via MathJobs using the provided job link, or submit by email if needed. If applying by email, send a single PDF with a statement of research interests and motivation, your CV with contact details for one or two academic references, and any publications, Master’s thesis abstract, or open-source contributions if applicable. Direct questions to [email protected].
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.