We have multiple openings for Machine Learning Graduate Student Interns to engage in practical research experience to further their educational goals. You will work on multidisciplinary projects, such as development of classical empirical and machine learning interatomic potentials , discovery of partial differential equations (PDEs) , numerical solutions of partial differential equations to model material behavior at continuum scale and analysis of large atomic datasets . These positions are in in the Equation of State Materials Theory Group of the Physics Division of the Physical & Life Sciences Directorate. These positions are expected to start between January-April of 2026 You will Develop parallel C/C++/Python codes to train, test and evolve (a) PDEs discovered from data, and (b) interatomic potentials developed from quantum simulations. Explore the use of machine learning methods to discover and evolve PDEs. Analyze results, provide weekly updates and present work at poster sessions Review literature in the field of study, document results and write papers. Perform other duties as assigned.
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Career Level
Intern
Number of Employees
5,001-10,000 employees