Job offer

The research programme will be developed jointly with the successful candidate, taking into account their expertise and interests within the scientific scope of PERSNET. Possible research directions include:

• Topological and geometric analysis of neural networks.
• Transformers, attention dynamics, tokenization and learned representations.
• Generative modelling on manifolds, stratified spaces and singular data.
• Diffusion and score-based models, including their connections with curvature, reach and tangent/normal geometry.
• Drifting models, kernelized transport, gradient flows associated with maximum mean discrepancy and reproducing kernel Hilbert spaces, and nonlocal partial differential equations.
• RKHS geometry, kernel mean embeddings and geometric statistical testing.

The main duties are to conduct original research, prepare scientific publications, present results at seminars and conferences, and participate in the scientific activities of the host team and the PERSNET project.

PhD or equivalent, * A strong mathematical background in one or more relevant areas, including probability, statistics, geometric inference, optimal transport, partial differential equations, stochastic processes, topological data analysis, kernel methods or mathematical machine learning.

• The ability to develop rigorous mathematical arguments and pursue original research with increasing independence.
• The ability to collaborate and communicate research effectively in English.

Previous experience with generative models, transformers or numerical experiments is welcome but is not required. Expertise in all the research directions listed above is not expected.