Computational Plasma Physics/Machine Learning Postdoc
About the role
The successful candidate will develop machine learning-enabled complexity reduction methods for multiscale plasma physics simulations. Research topics include reduced-order modeling, surrogate modeling, uncertainty quantification, and scalable computational algorithms for plasma applications.
This position is an exciting opportunity to join a highly collaborative team and work alongside leading experts from a wide range of disciplines. The project brings together statisticians, applied mathematicians, and physicists within T-5, while also fostering close collaborations with other groups at Los Alamos—including the Intelligence and Space Research Division and the Accelerator Operations and Technology Division. Beyond Los Alamos, the work connects with teams across the U.S. and around the world, offering broad opportunities to engage and contribute at the forefront of science.
Minimum Job Requirements
- A Ph.D. in computational plasma physics, applied mathematics, computational science or a closely related field, completed within the past five years or soon to be completed
- Experience with Machine Learning for science applications
- Experience programming in Fortran, C++, Python
- Experience with numerical methods
- Demonstrated research ability in computational plasma physics and/or machine learning, as evidenced by publications, reports, presentations, awards, etc.
- Excellent communication skills (both oral and written)
Education/Experience
By the start of the position the applicant must have completed a PhD in a relevant STEM field.
Desired Qualifications
- Ability to work independently as well as to work effectively as a part of a team in a multi-disciplinary environment and interact with people with a variety of expertise
- Willingness and flexibility to take initiative and pursue new research directions
Work Environment
The work location for this position is onsite and located in Los Alamos, NM. All work locations are at the discretion of management.