Computational Scientist, Differentiable Physics

Periodic Labs
Menlo Park
Workplace: OnsiteFull timeUSD 250,000 - 350,000Function: Research & Scientific (R&D)Education: phdSkills: ["Ownership","Good judgment","Building from scratch","Diagnostic/problem-solving","Collaboration"]

Build and extend differentiable, accelerator-ready continuum-physics simulations, with a focus on fluid dynamics and multi-scale/multi-physics problems. Implement numerical methods from governing equations and papers, diagnose convergence and stability, and validate against experiments or high-fidelity benchmarks. Combine physics-based solvers with deep learning for surrogate modeling, inverse problems, parameter estimation, and optimization using automatic differentiation and tools like JAX or PyTorch.

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FursaFursa
Periodic Labs
Periodic Labs
2 days ago

Computational Scientist, Differentiable Physics

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Source: Company careers pageValidated by: Fursa AI
Last checked: 7 hours agoStatus: Live

Job Summary

Build and extend differentiable, accelerator-ready continuum-physics simulations, with a focus on fluid dynamics and multi-scale/multi-physics problems. Implement numerical methods from governing equations and papers, diagnose convergence and stability, and validate against experiments or high-fidelity benchmarks. Combine physics-based solvers with deep learning for surrogate modeling, inverse problems, parameter estimation, and optimization using automatic differentiation and tools like JAX or PyTorch.
Location: Menlo Park
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Build and extend differentiable solvers for continuum simulation, especially fluid dynamics and multi-scale/multi-physics problems.
  • •Implement numerical methods from equations and papers, and diagnose convergence, stability, and modeling failures.
  • •Combine simulations 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, and create datasets/evaluations to accelerate scientific workflows.

Pay and Benefits

Salary: USD 250,000 - 350,000
Equity and Bonus:Equity
Perks:Equity

Key Requirements

  • •PhD or equivalent research experience in applied mathematics, computational science, physics, engineering, computer science, or related fields.
  • •Code-level experience building or substantially modifying PDE solvers, numerical methods, or differentiable simulations.
  • •Deep expertise in a continuum-physics domain (with at least some experience in fluid dynamics).
  • •Experience building, training, and evaluating deep-learning models for physical systems.
  • •Strong Python and software-engineering skills, especially JAX, PyTorch, Julia, or C++.
Experience:Continuum physicsFluid dynamicsMultiphysicsDeep learningNumerical methodsPDE solvers
Education:PhD / Doctorate
Skills:OwnershipGood judgmentBuilding from scratchDiagnostic/problem-solvingCollaboration
Tech Stack:PythonJAXPyTorchJuliaC++Automatic differentiationGPUsTPUs

Eligibility

Work Authorization:Sponsorship available.

Company Brief

Periodic Labs
Periodic Labs (periodic.com) — Public information about this company’s products, industry focus, size, funding, and headquarters is not available. Check the company’s official profiles or contact them directly for accurate details about their offerings and operations.
Industry: Other
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