deep learning
ABOUT THE ROLE
Periodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to build differentiable, accelerator-ready simulations for industrially relevant continuum-physics problems.
You should be equally comfortable with governing equations, solver code, and deep learning. We are open to expertise in any area of continuum-physics, with at least some experience in fluid dynamics. You will work on building simulation capabilities in challenging, data-limited domains requiring a mix of physics-based and empirical approaches.
WHAT YOU’LL DO
- Build and extend differentiable solvers for continuum simulation (including but not limited to fluid dynamics), especially multi-scale and multi-physics problems.
- Implement numerical methods from equations and papers, and diagnose convergence, stability, and modeling failures.
- Combine simulation with deep learning for surrogate modeling, learned physics, inverse problems, parameter estimation, and optimization.
- Use automatic differentiation and modern accelerators with JAX or PyTorch to make simulations scalable and trainable.
- Validate models against experiments, trusted benchmarks, or high-fidelity simulations.
- Create datasets and evaluations to guide the development of LLMs to accelerate and automate these tasks.
YOU WILL THRIVE HERE IF YOU HAVE
- A PhD or equivalent research experience in applied mathematics, computational science, physics, engineering, computer science, or a related fiel