Call of chairs Attractivité

Overview

PEPR AI Chairs program offers exceptionally talented AI researchers the opportunity to establish and lead a research program and team for a duration of 4 years in France.

This action extends efforts launched in the first period under the Choose France program. This PEPR AI Chairs program is open to both junior and senior researchers, with the primary criterion being an outstanding research track record. Applications are welcome from individuals who earned their PhD either in France or abroad. Candidates who obtained their PhD in France and wish to return must demonstrate substantial international research experience, including mentoring students and managing scientific projects.

This call is open to filling at least 5 positions. Candidates must demonstrate outstanding track records in machine learning (ML), particularly in the thematic areas of the PEPR IA :

  • Frugality
  • Embedded AI
  • Trustworthiness
  • Distributed AI
  • Mathematical foundations of AI 

To attract the best talents, the program will provide customizable integration options tailored to candidates varying degrees of seniority and experience. Candidates may be hosted in laboratories associated with the PEPR, in laboratories not affiliated with the program but with clear and strong synergies with the PEPR IA projects.

The selected candidates may be recruited by any University or Research institute in France.  Candidates must propose at least one laboratory to host their research project. They must contact the director of this laboratory and explain in their research proposal how their project aligns with the chosen laboratory, the PEPR AI objectives and how they complement the works already targeted by the ongoing PEPR projects.

The final appointment decision will be made by the PEPR AI Committee after candidates are selected and interviewed by a scientific recruitment committee. Candidates are expected to contribute to the overall French AI Strategy, and applications that align with the PEPR projects and other initiatives of the French AI Strategy, like Cluster AI institutes are encouraged.

The selection process will be conducted on a rolling basis, with regular evaluation sessions throughout the year.

Contact : contact@pepr-ia.fr


Autres projets

 NNawaQ
NNawaQ
NNawaQ, Neural Network Adequate Hardware Architecture for Quantization (HOLIGRAIL project)
Voir plus
 Package Python Keops
Package Python Keops
Package Python Keops for (very) high-dimensional tensor calculations (PDE-AI project)
Voir plus
 MPTorch
MPTorch
MPTorch, a PyTorch-based framework for simulating and emulating custom precision DNN training (HOLIGRAIL project)
Voir plus
 CaBRNeT
CaBRNeT
CaBRNeT, a library for developing and evaluating Case-Based Reasoning Models (SAIF project)
Voir plus
 SNN Software
SNN Software
SNN Software, Open Source Tools for SNN Design (EMERGENCES project)
Voir plus
 SDOT
SDOT
SDOT, A C++ and Python library for Semi-Discrete Optimal Transport (PDE-AI project)
Voir plus
 FloPoCo
FloPoCo
FloPoCo (Floating-Point Cores), a generator of arithmetic cores and its applications to IA accelerators (HOLIGRAIL project)
Voir plus
 Lazylinop
Lazylinop
Lazylinop (Lazy Linear Operator), a high-level linear operator based on an arbitrary underlying implementation, (SHARP project)
Voir plus
 CAISAR
CAISAR
CAISAR, a platform for characterizing artificial intelligence safety and robustness
Voir plus
 P16
P16
P16 or to develop, distribute and maintain a set of sovereign libraries for AI
Voir plus
 AIDGE
AIDGE
AIDGE, the DEEPGREEN project's open embedded development platform
Voir plus
 Jean-Zay
Jean-Zay
Jean Zay or the national infrastructure for the AI research community
Voir plus
 ADAPTING
ADAPTING
Adaptive architectures for embedded artificial intelligence
Voir plus
 CAUSALI-T-AI
CAUSALI-T-AI
When causality and AI teams up to enhance interpretability and robustness of AI algorithms
Voir plus
 EMERGENCES
EMERGENCES
Near-physics emerging models for embedded AI
Voir plus
 FOUNDRY
FOUNDRY
The foundations of robustness and reliability in artificial intelligence
Voir plus
 HOLIGRAIL
HOLIGRAIL
Hollistic approaches to greener model architectures for inference and learning
Voir plus
 PDE-AI
PDE-AI
Numerical analysis, optimal control and optimal transport for AI / "New architectures for machine learning".
Voir plus
 REDEEM
REDEEM
Resilient, decentralized and privacy-preserving machine learning
Voir plus
 SAIF
SAIF
Safe AI through formal methods
Voir plus
 SHARP
SHARP
Sharp theoretical and algorithmic principles for frugal ML
Voir plus