This is a one-year, benefits-eligible position that is potentially renewable on an annual basis contingent upon satisfactory performance and availability of funding.
In this work, the research associate will focus on either Scientific Machine Learning (SciML) surrogates, with coupling to multi-physics solvers, or scalable high order methods, with coupling to SciML models, for magnetic reconnection problems involving radiation losses or magnetized turbulence. The research associate will play a leading role in model integration within the Parthenon framework, bringing together new time stepping methods for stiff terms, new high order methods, and SciML surrogate models being developed by members of the team.
Completion of a Ph.D. in mathematics, physics, astrophysics, or a related area is required before Fall 2026. Knowledge of parallel programming and experience developing methods for 2D and 3D problems are critical. Experience working with open source software frameworks and/or using modern open source code development methodology is highly desirable, as is experience with numerical methods pertaining to fluid dynamics or plasma physics.
Under this National Nuclear Security Administration (NNSA) funded project, candidates with United States citizenship are preferred. However, with approval from DOE-NNSA, citizens from outside of the United States will be considered. Please contact Professor Andrew Christlieb if you wish to know more about this posting (christli@msu.edu).
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