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Data Scientist

Fuse · San Leandro, California · On-site

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About Fuse

Fuse Energy is building the future of energy with cheaper tariffs, better service, and greener goals. Switch to Fuse Energy today. Backed by Accel.

About the role

You will build our in-house simulation stack — the models that predict what our fusion machines will do, and the inference machinery that pulls physics out of shot data. This is not a “fit a curve to a dashboard” data science role. Build a coupled simulation framework for our DPF: pulsed-power circuit, sheath formation and run-down (snowplow / slug / Lee-type models, progressing toward MHD), pinch and instability development, and neutron and X-ray production.

What they're looking for

  • PhD in Physics, Applied Mathematics, Plasma Physics, Computational Science, Nuclear Engineering, or a closely related field. An exceptional Master's candidate with a strong publication record will be considered
  • Strong academic record, with a target GPA of 3.8+ from a competitive program
  • Demonstrable depth in at least one of: magnetohydrodynamics, kinetic plasma theory, radiation transport, or pulsed-power circuit modeling. Familiarity with the others
  • Fluency in vector and tensor calculus, Maxwell's equations in arbitrary geometries, hyperbolic PDEs and numerical schemes, Bayesian statistics, and optimization under constraints
  • Production-grade Python using NumPy, SciPy, xarray, JAX, or PyTorch for differentiable physics, with comfort dropping into C++ or Fortran when Python isn't fast enough
  • Experience writing simulation code from scratch, not just running someone else's solver
More about this role

We are building one of the most potentially consequential companies of the century. Our mission is to accelerate the world's transition to fusion energy while safeguarding humankind. This is not a normal company. This is not a normal job. We are committed for the long term to win.

You will build our in-house simulation stack — the models that predict what our fusion machines will do, and the inference machinery that pulls physics out of shot data. This is not a “fit a curve to a dashboard” data science role.

Build a coupled simulation framework for our DPF: pulsed-power circuit, sheath formation and run-down (snowplow / slug / Lee-type models, progressing toward MHD), pinch and instability development, and neutron and X-ray production.

Implement radiation source-term models: thermonuclear vs. beam-target neutron-yield decomposition, bremsstrahlung and line-emission spectra, and anisotropy.

Develop surrogate and reduced-order models so designers and physicists can iterate on parameter sweeps without standing up an HPC job each time.

Build the data pipeline that ingests every shot's diagnostic stream, including Rogowski coils, B-dots, silver activation, time-of-flight neutron...

Read the full posting on Fuse's site ↗

Engineering / Technical Ops.

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