Burning Plasma Scientist

@ Thea Energy
Thea Energytheaenergy.com

Burning Plasma Scientist

Posted 4 months ago

This job is still taking applications, but it's been up a while.

About the job

Thea Energy creates innovative fusion solutions using stellarator physics, engineering, and machine learning to offer sustainable, zero-emission energy. The role involves modeling plasma behavior, developing ML surrogates, and collaborating on coil designs to optimize fusion performance.

Requirements

  • PhD in Plasma Physics or related
  • Programming in Python, Fortran, C++, MATLAB
  • Experience with kinetic or MHD codes
  • Collaborative problem solving
  • Passion for fusion research

Qualifications

  • Research experience in plasma physics
  • Familiarity with energetic particle diagnostics
  • Creative, team-oriented mindset
  • Strong analytical skills

Full job description

About Thea Energy: 

Thea Energy is leveraging recent breakthroughs in stellarator physics and engineering to create a faster and simpler approach to commercializing fusion energy. The company is reinventing the stellarator using computer-controlled arrays of planar coils thereby replacing the intricate, complex modular magnets required in all other stellarator architectures. Thea Energy is on a mission to create a limitless source of zero emission energy for a sustainable future.  

Position Overview:

Thea Energy is looking for a highly motivated Burning Plasma Scientist to join our Physics team, focusing on modeling energetic particle confinement and transport. In this role, you will run high-fidelity simulations and pioneer the use of Machine Learning surrogates to accelerate our design cycles. You will work directly with our optimization team to ensure our magnetic topologies manage fast-ion loss and heat loads effectively.

Key Responsibility Areas:

  • Execute and analyze high-fidelity simulations using particle-following codes to assess alpha particle and neutral beam ion confinement in 3D magnetic geometries
  • Investigate Alfvén Eigenmode (AE) stability using kinetic-MHD hybrid codes and assess their impact on energetic particle transport
  • Develop reduced-order models and ML surrogates trained on simulation databases to enable real-time prediction of pressure profiles
  • Collaborate with the engineering team to iterate on coil designs and divertor configurations to minimize fast-ion losses
  • Develop synthetic diagnostics (e.g. Fast-Ion Loss Detectors, Neutron Cameras) to bridge the gap between our models and future experimental campaigns

Ideal Experience & Skillsets:

  • PhD in Plasma Physics, Computational Physics, or related field (or Master’s / Bachelor’s with equivalent research experience)
  • Proficiency in programming languages commonly used in scientific computing (e.g. Python, Fortran, C++, or MATLAB)
  • Creative problem solving and proven ability to thrive in collaborative environments such as startups or research groups
  • Willingness to collaborate with a diverse team of scientists and engineers within the company and externally
  • Passion for advancing the field of fusion energy research
  • Familiarity with kinetic, MHD or particle-following codes
  • Experience developing Machine Learning surrogates for physical systems
  • Familiarity with energetic particle diagnostics

Company Benefits:

  • Salary range $135,000-$210,000
  • Comprehensive health benefits (e.g. medical/dental/vision)
  • Employee equity stock options
  • 20 days PTO

It’s not necessary to meet all of the skillsets outlined above. Please feel free to send us a note and tell us why you would still be a great fit for this role or Thea Energy.  

Diversity and Inclusion:  

Thea Energy is an equal opportunity employer committed to creating a company of diverse backgrounds. By creating a diverse environment, we will bring new ideas and approaches to solving some of the world’s hardest (and most important) problems. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, sexual orientation, gender identity or expression, national origin, family or marital status, age, disability, veteran’s status, or other characteristic protected by applicable laws and regulations.

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