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

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.