Principal Applied Plasma Data Scientist

• Develop and validate machine‑learning frameworks for FRC plasma‑dynamics, leveraging physics‑grounded ML approaches (e.g., differentiable physics, PINNs, surrogate modeling, or hybrid physics+ML systems)
• Integrate ML models with existing simulation tools and experimental‑diagnostic pipelines to enable hybrid physics + data‑driven validation workflows
• Reproduce experimental plasma conditions within ML modeling workflows to support interpretation of magnetic diffusion, circuit response, and diagnostic measurements
• Generate analysis scripts for post‑processing, parsing model outputs, and visualizing correlations between ML‑based predictions and experimental results
• Collaborate with plasma‑physics, pulsed‑power, and transient‑magnetics experts to expand the fidelity and fusion‑relevance of ML‑enabled modeling frameworks
• Communicate results with rigor and clarity through reports, presentations, and technical visualizations for research stakeholders
• PhD in Physics, Engineering, Applied Math, Computational Science, Machine Learning, or related field with emphasis on physics‑grounded modeling
• 10+ years of industry or research‑lab experience applying machine learning in experimental, simulation‑driven, or scientific R&D environments
• Expertise with physics‑informed or physics‑constrained ML methods, such as differentiable physics, PINNs, scientific ML, surrogate modeling, or reduced‑order models for complex physical systems
• Experience integrating ML models with scientific‑computing workflows, simulation tools, or experimental diagnostics
• Proficiency scripting for data parsing, model analysis, and visualization (MATLAB, Python, or similar; Fortran or HPC exposure valuable)
• Familiarity with magnetic diffusion, circuit coupling, or plasma‑dynamics modeling in pulsed‑power or electromagnetics systems

None flagged.