AI Scientist: Physics Simulation Foundation

Mistral; Paris; On-site; EUR 90,000 - 150,000; Full time

Job summary

Mistral is seeking AI Scientists to push the frontier of AI-accelerated simulation within AI4Engineering Science. You will research and train foundational physics models, curate high-fidelity datasets, design architectures, and evaluate them against rigorous engineering validation standards.

Collaborate with the broader research organization to ensure the models are general enough to become the backbone of multiple downstream products, not just a single solution.

Qualifications

• PhD or Master's in CS/AI or engineering science required.
• Strong ML experience with model architectures, training dynamics, and evaluation.
• Experience with simulation or surrogate modelling.
• Proficient Python coding in Linux/HPC.
• Fluent English and strong communication.

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 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.

Skills

Python

Machine Learning

HPC environments

English proficiency

Education

PhD or Master's in CS/AI or engineering science

Tools

OpenFOAM

LS-DYNA

ANSYS

COMSOL

Abaqus

Fluent

STAR-CCM+

Job description

Mistral is seeking AI Scientists to push the frontier of AI-accelerated simulation within AI4Engineering Science. You will research and train foundational physics models, curate high-fidelity datasets, design architectures, and evaluate them against rigorous engineering validation standards.

Collaborate with the broader research organization to ensure the models are general enough to become the backbone of multiple downstream products, not just a single solution.