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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.
What they're looking for
- A PhD or equivalent research experience in applied mathematics, computational science, physics, engineering, computer science, or a related field
- Code-level experience building or substantially modifying PDE solvers, numerical methods, or differentiable simulations
- Deep expertise in at least one continuum domain, with breadth across domains or a demonstrated ability to learn new physics quickly
- Meaningful experience building, training, and evaluating deep-learning models for physical systems
- Strong Python and software-engineering skills, especially JAX, PyTorch, Julia, or C++
- Experience applying simulation to realistic scientific or engineering problems, not only clean academic benchmarks
More about this 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.
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...
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