Description du sujet de thèse

This PhD project focuses on designing a neuromorphic analog computer capable of efficiently solving NP-hard combinatorial optimization problems, such as Max-Cut or 3-SAT. It leverages an innovative reconfigurable analog circuit platform (FPAA – Field-Programmable Analog Array), combining the energy efficiency and fast convergence of analog computing with the flexibility of digital processing. Inspired by Hopfield/Ising networks and non-linear dynamics, the research aims to overcome limitations inherent to traditional digital architectures.

Contexte de travail

The PhD student will join the Terahertz NDE and Nonlinear Dynamics team of the Georgia Tech-CNRS International Research Lab 2958, based at Georgia Tech Europe in Metz. This group is renowned for its expertise in nonlinear dynamics, terahertz imaging, and physics-based computation. The project is part of the ANR-funded AATLAS programme and involves close collaboration with Professor Jennifer Hasler (Georgia Tech Atlanta), a world leader in FPAA-based architectures. The student will benefit from high-level supervision and a dynamic research environment, with frequent interaction with leading scientists such as Prof. David Citrin and Dr. Alexandre Locquet.

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

This project entails technical challenges related to optimizing the FPAA hardware for large-scale optimization problems. Unforeseen issues with neuromorphic system performance may arise. The candidate is expected to demonstrate strong time-management skills, especially in the context of ongoing collaboration with Georgia Tech Atlanta. Access to the laboratory also requires ZRR (Restricted Access Zone) clearance.

Informations complémentaires

Applicants should hold a Master's degree or equivalent in electronics, applied physics, or a related field. A strong command of analog circuits, microelectronics, or reconfigurable computing platforms (FPAA, FPGA) is required. A strong interest in unconventional computing, nonlinear dynamics, and neuromorphic systems is expected. Prior experience with combinatorial optimization or complex systems would be an advantage. The PhD candidate must demonstrate autonomy and be ready to contribute to a highly collaborative, interdisciplinary, and international research environment. Excellent proficiency in scientific English is essential.