Research Scientist I/II, Multiscale & Multiphysics Simulations

Your role will focus on building next-generation in silico multiphysics and multiscale simulation capabilities that power AI-driven scientific discovery. You will develop high-fidelity digital representations of complex physical systems spanning chemical and mechanical processes, transport phenomena, and electromagnetic behavior and integrate them into autonomous discovery and experimental pipelines.

You will work on integrating simulation methods—such as finite element modeling, computational fluid dynamics, phase-field methods, and TCAD-style transport/process modeling—into scalable, programmatic, and agent-driven systems that enable real-time digital twins, simulation-informed decision-making, and autonomous closed-loop workflows

• Develop and deploy robust multiphysics models across coupled domains (e.g., thermal, fluid, structural, electromagnetic, chemical), using methods such as coarse-grained, mesoscale, FEM, and CFD techniques.
• Build integrated multiscale frameworks that connect atomistic, mesoscale, and continuum representations to model materials and devices.
• Design and implement programmatic, agent-driven simulation workflows that can autonomously configure, execute, and refine simulations within closed-loop discovery workflows.
• Create scalable, GPU-accelerated simulation pipelines, data infrastructure, and interoperable APIs that connect commercial tools (e.g., COMSOL, ANSYS) and custom solvers deploying on cloud-based, high-throughput computing environments
• Collaborate with AI, software, and automation teams to orchestrate and deploy closed-loop discovery workflows, integrating computational predictions with robotic and cloud-based laboratory platforms to enable automated experiment–simulation feedback cycles and accelerated R&D.

• PhD in Mechanical Engineering, Chemical Engineering, Aerospace Engineering, Materials Science, or a related field.
• Extensive experience with multiphysics simulation methods and numerical algorithms, including FEM, CFD, TCAD/process simulation, mesoscale modelling, or related techniques.
• Strong foundation in coupled physical phenomena, including heat transfer, fluid dynamics, structural mechanics, mass transport, diffusion, electromagnetism, and reaction kinetics.
• Experience applying simulation to real-world systems in industrial settings such as semiconductors, chemical processing, aerospace, or materials manufacturing.
• Solid programming skills in Python and building simulation workflows, automation pipelines, or custom numerical models.

• Experience bridging atomistic simulations with one or more additional simulation domains including coarse-grained, finite-element and continuum models.
• Familiarity with machine learning approaches applied to physical simulations (e.g., surrogate models, neural operators, physics-informed neural networks), along with experience leveraging GPU acceleration and programmatic optimization for scalable simulations
• Experience integrating simulation frameworks into digital twin systems, real-time decision environments, or closed-loop control workflows.
• Background applying simulation to complex materials and process domains such as thin-film deposition, micro/nano-fabrication, or reactive transport, with an understanding of processing–structure–property relationships.

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