Sigma NovaAbout this role
Summary: The Research Scientist will develop physics-informed machine learning models and surrogate modeling techniques for industrial applications in aeronautics, automotive, and NDT. The role involves building fast, accurate surrogates using PINNs and neural operators, extending foundation models to physical domains, and...
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