Postdoctoral Researcher in Mathematics of Machine Learning — PERSNET

Ecole Normale Supérieure; France; On-site; EUR 36,000 - 48,000; Full time

Job summary

Ecole Normale Supérieure in France invites applications for an 18-month, full-time postdoctoral position in the mathematical and statistical foundations of machine learning, working with the candidate and the PERSNET project.

The role focuses on rigorous theoretical analysis, development of publications, and participation in seminars and team activities, with opportunities to explore directions such as neural networks analysis, transformers, and diffusion models.

Qualifications

• PhD or equivalent required.
• Strong background in probability, statistics, and mathematical ML.
• Ability to communicate research effectively in English.

Responsibilities

• Conduct original research in mathematical and statistical foundations of ML.
• Prepare scientific publications and present results at seminars and conferences.
• Participate in the scientific activities of the host team and the PERSNET project.

Skills

Strong mathematical background

Rigorous argumentation

English communication

Education

PhD or equivalent

Job description

Organisation/Company Ecole Normale Supérieure Research Field Mathematics » Statistics Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 30 Nov 2026 - 12:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Applications are invited for an 18-month, full-time postdoctoral research position in the mathematical and statistical foundations of machine learning, working with Eddie Aamari.

The position is supported by the PERSNET project within the French PEPR Artificial Intelligence programme, funded by France 2030.

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.

Requirements

Research Field Mathematics » Statistics Education Level PhD or equivalent

Skills/Qualifications

Applicants should demonstrate:

• 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.