AI Scientist - Physics Models

Mistral is looking for AI Scientists with deep expertise in engineering sciences and machine learning to push the frontier of AI-accelerated simulation. Within AI4Engineering Science, you will research and train foundational physics models which are substantially more capable than what exists today and can be fine-tuned for downstream applications by both customers and internal teams.

Responsibilities

Research and train novel foundation models for physics simulation, pushing past today's state of the art in accuracy, generalization, and scale; Design and run large-scale simulation campaigns using domain-specific solvers to build the high-fidelity datasets foundational physics models need; Investigate architectures and training strategies (e.g. multi-fidelity training, pretraining objectives, scaling behavior) that let a single foundation model transfer and fine-tune well across diverse engineering tasks; Rigorously evaluate model coverage, accuracy, and robustness against industry validation standards, and diagnose failure modes arising from data gaps or architecture limitations; Stay on top of the latest developments in the scientific community and contribute to Mistral's standing at the frontier of AI-for-engineering research

Requirements

PhD or Master's in CS/AI or an engineering science: Mechanical Engineering, Electrical Engineering, Computational Fluid Dynamics, Structural Mechanics, EDA, Semiconductor Engineering, or a related field; Strong, hands-on machine learning expertise with a deep understanding of model architectures, training dynamics, and evaluation methodology is core to this role; You have developed ML methods for simulation or surrogate modelling; You write clean, readable Python code and are comfortable in Linux/HPC environments; Fluent English with excellent communication skills, able to explain technical simulation and ML concepts to both engineering and non-technical audiences; Self-directed, you don't need detailed roadmaps to make progress; Low-ego, collaborative, and eager to learn at the intersection of simulation and ML; Demonstrated success through industrial projects, academic work, or personal projects

Nice to have

Have industrial or academic experience with simulation solvers (e.g. OpenFOAM, LS-DYNA, ANSYS, COMSOL, Abaqus, Fluent, STAR-CCM+, PowerFlow, NekRS, Tau/CODA, JAX-Fluids, or equivalent; Cadence/Synopsys/Siemens EDA or equivalent); Have experience automating large-scale simulation campaigns on HPC clusters; Have contributed to a large open-source or industry codebase; Have publications in engineering or ML venues (AIAA, ASME, JFM, NeurIPS, ICLR, etc.)

Benefits

Comprehensive benefits package designed to support your well-being, growth, and work-life balance; Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks