Advance the frontier of physics-informed AI by building hybrid models that merge physics-based solvers with machine learning.
Requirements
- Expertise in scientific computing, numerical methods, and model development.
- Experience with TensorFlow/PyTorch and simulation frameworks.
- Experience in multi-scale modeling or uncertainty quantification.
Responsibilities
- Build hybrid models that merge physics-based solvers with machine learning.
- Experiment with neural operators, PINNs, and surrogate modeling for high-fidelity CFD data.
- Collaborate with engineers to test and validate models on real-world geometries.
- Productionize research that advances the frontier of physics-informed AI.
Other
- PhD or strong research background in Mechanical/Aerospace Engineering, Applied Physics, or Machine Learning.
- Hands-on, iterative, and results-oriented researcher.
- Prior industry collaboration or customer-facing applied research.
- All roles are full-time and based in person at our San Francisco office.
