About the Role

We’re looking for an expert in mesoscale, multiphysics modeling and simulation to support the development of machine-learning physics surrogates.

This is a flexible, on-demand engagement, with work assigned based on project and task needs.

What You’ll Do

Develop and refine algorithms for mesoscale and multiphysics simulations
Design and run simulations to generate high-quality training and evaluation data for ML physics surrogates
Assess simulation outputs for physical accuracy, numerical stability, and suitability for machine learning
Contribute domain expertise to the design and validation of physics-based datasets and surrogate models
Document methodologies, assumptions, and results clearly for technical teams

PhD or equivalent expertise in computational physics, applied mathematics, engineering, materials science, or a closely related field
Deep experience with mesoscale modeling, multiphysics simulation, and numerical methods
Experience developing or modifying scientific simulation algorithms
Strong understanding of numerical modeling, physical systems, and simulation data generation
Proficiency with relevant scientific computing tools and programming languages such as Python, C++, Julia, MATLAB, or similar
Professional fluency in English

Experience developing or training ML surrogate models / reduced-order models for physical systems
Experience generating large-scale simulation datasets for machine learning
Familiarity with scientific ML, physics-informed ML, or operator-learning approaches
Experience with HPC or parallel scientific computing