Research Scientist : Computational Physics & AI (PINNs)

Join our team as a Research Scientist in Computational Physics & AI. In this role, you will work at the intersection of numerical physics, physics-informed machine learning, and surrogate modeling. You will focus on high-impact industrial domains such as aeronautics, automotive, and non-destructive testing. Your responsibilities will include building fast and accurate surrogates, extending foundation model thinking to physical domains, designing architectures that respect physical structure, and owning projects from research to demonstrators.

Responsibilities

• Étendre la pensée des modèles de fond aux domaines physiques, en développant des stratégies qui fonctionnent avec des ensembles de données physiques rares, bruyantes ou biaisées.
• Posséder des projets de recherche de bout en bout, de l'hypothèse aux expériences, en passant par l'entraînement sur l'infrastructure GPU.

Requirements

• A PhD or Master's degree with strong research/applied experience in a related field is preferred.
• Concevoir et développer des modèles de substitution rapides et précis pour émuler des simulations coûteuses ou des processus physiques.
• Education / experience: PhD (preferred) or Master’s with strong research/applied experience in Computational Physics, Numerical Physics, Scientific Computing, Machine Learning, or a closely related quantitative field.
• Physics + ML depth: strong grounding in physical modeling and numerical methods (e.g., PDEs, boundary conditions, discretization, simulation workflows) plus solid deep learning fundamentals.
• Research discipline: ability to design rigorous experiments, debug systematically, and produce clear, defensible conclusions.
• Engineering maturity: strong Python skills; experience with PyTorch and training on GPU(s)/clusters

Preferred

• Experience with Neural Operators, surrogate modeling, PINNs, geometric deep learning, mesh/point cloud learning, or scientific ML toolchains.
• Domain exposure to aeronautics, automotive, or industrial R&D environments
• Experience with inverse problems, uncertainty quantification, calibration, or safety/robustness constraints in scientific/industrial ML.
• Publications or open artifacts in ML/physics venues (NeurIPS / ICML / ICLR; NeurIPS ML & Physical Sciences workshop; relevant physics/engineering journals/conferences).