Post-doc (M/F): Equilibrium propagation for skyrmionic neuromorphic computing

Job Description

Missions

The project aims at developing new learning methods for neuromorphic devices based on the dynamics of magnetic skyrmions. The main objective is to use the equilibrium propagation algorithm — EqProp — to design local learning rules adapted to skyrmionic systems, validate them through simulations, and contribute to their experimental implementation.
The first objective will be to formulate and study local learning rules compatible with the nucleation, propagation and interaction of magnetic skyrmions in nanodevices. The second objective will be to develop an inverse-design code combining micromagnetic simulations and machine learning methods, in order to optimize skyrmionic device geometries, materials and control parameters. The third objective will be to use this code to simulate and train skyrmion-based neuromorphic networks, and to contribute to their experimental validation in collaboration with the team.

Activity

• Development of neuromorphic learning models based on the equilibrium propagation algorithm
• Design of local learning rules for magnetic skyrmion systems
• Development of an inverse-design code combining micromagnetic simulations and machine learning
• Micromagnetic simulations of skyrmion nucleation, propagation and interaction
• Optimization of skyrmionic devices for neuromorphic computing
• Machine learning simulations applied to artificial intelligence tasks
• Analysis of the performance, robustness and energy efficiency of the proposed architectures
• Interaction with experimental researchers to guide device fabrication and characterization
• Participation in the experimental implementation of learning rules in magnetic nanodevices
• Writing scientific publications and presenting results at international conferences

Your Profil

Skills

• Strong experience in numerical simulation and/or physical modelling
• Knowledge or strong interest in nanomagnetism, spintronics or magnetic skyrmion dynamics
• Expertise in machine learning, optimization or learning in physical systems. Expertise with Equilibrium Propagation will be strongly appreciated.
• Very good knowledge of Python
• Experience with machine learning libraries such as PyTorch, JAX, TensorFlow or equivalent
• Knowledge of micromagnetic simulations
• Interest in inverse-design methods, differentiable optimization or neuromorphic computing
• Ability to develop robust, documented and reusable scientific code
• Motivation to work at the interface between theory, numerical simulation and experiment
• Experience in the fabrication or characterization of magnetic nanodevices will be appreciated, but is not mandatory

Your Work Environment

Les travaux seront réalisés au Laboratoire Albert Fert, dans l'équipe "Physique Neuromorphique" explorant l'utilisation des nanodispositifs et leurs multiples fonctionnalités pour le calcul bio-inspiré. L'équipe comprend deux chercheuses CNRS permanentes, deux chercheurs Thales, 4 post-doctorants et 4 doctorants.

Constraints and risks

NA

Compensation and benefits

Compensation

Between 3131,32 and 4341,70 € gross per month according to research experience

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions