PhD Position: Theoretical Foundations of Self-Supervised Learning
Fully funded PhD position on theoretical foundations of self-supervised learning at University of Lorraine, focusing on representation stability, interpretability, and efficiency.
The SiMul team at the University of Lorraine is offering a fully funded PhD position on the theoretical foundations of self-supervised learning, focusing on representation stability, interpretability, and efficiency.
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 at univ-lorraine.fr, ricardo.borsoi at univ-lorraine.fr, konstantin.usevich at univ-lorraine.fr) with:
- An academic CV
- A short explanation of research interests and motivation for this position
Tags: PhD position, self-supervised learning, low-rank tensor models, representation stability, interpretability, efficiency, AI research, University of Lorraine