Research Scientist : Computational Physics & AI (PINNs / Surrogate Modeling)

Description

This role sits at the intersection of numerical physics / simulation, physics-informed ML, and surrogate modeling with a strong pull toward high‑impact industrial domains such as aeronautics, automotive, and non‑destructive testing (NDT).

We are open in how we frame the position (Research Scientist vs Applied Scientist): what matters is a profile that can do serious research while keeping a clear path to deployment and operational impact, including collaborations with labs and industrial partners when relevant.

What you’ll work on

• Physics-informed & surrogate modeling (PINNs / Neural Operators / Neural PDEs) Build fast, accurate surrogates to emulate expensive simulations or physical processes (e.g., PDE-driven systems, complex boundary conditions, multi-physics settings). Explore approaches such as Fourier Neural Operators, operator learning, PINNs, and hybrid ML+numerics methods.
• Computational / numerical physics meets foundation models Help us extend “foundation model thinking” to physical domains: pretraining, adaptation, and evaluation for scientific/industrial data (fields, meshes, sensor arrays, tomography/ultrasound-like signals, etc.). Develop strategies that work under scarce, noisy, irregular, or biased physical datasets.
• Architecture, inductive biases, and messy reality Design architectures that respect physical structure: invariances/equivariance, geometry, irregular spatial/temporal grids, mesh-based data, multimodal measurement pipelines. Investigate failure modes: when deep learning breaks in physics settings, why it breaks, and what to do about it.
• From research to demonstrators Own projects end-to-end: hypotheses → experiments → training on GPU infrastructure → robust evaluation → prototype integration. Define evaluation protocols aligned with constraints like calibration/uncertainty, robustness, traceability, and cost of error.

Profile

• 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

Nice to have

• 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).

Recruitment process

• Recruitment prescreen (remote 30-45min)
• Scientific deep dive (remote-45min)
• Half-day of scientific interviews (Architecture - Coding - Research talk) + Culture fit
• References call

Company

Foundation models for scientific and industrial data.