Despite their success, self-supervised approaches and foundation models still lack a thorough theoretical understanding. This project aims to bridge that gap by exploring connections between AI models and low-rank tensor decompositions, providing a rigorous mathematical framework to address key questions:
When are learned representations interpretable and stable?
How do models perform on heterogeneous data (e.g., federated or personalized learning)?
Can smaller, energy-efficient models achieve strong performance on specialized tasks?
Position Details
Location: Nancy, France
Funding: Fully funded
Candidate Profile: Master's degree (or equivalent) in applied mathematics or an AI-related field. A strong mathematical background is required.
More details: https://cran-simul.github.io/assets/jobs/sujetThese_LENTILLE_2025.pdf
How to Apply
Interested candidates should send their application to David Brie, Ricardo Borsoi, and Konstantin Usevich (david.brie@univ-lorraine.fr, ricardo.borsoi@univ-lorraine.fr, konstantin.usevich@univ-lorraine.fr) with:
An academic CV
A short explanation of research interests and motivation for this position
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