Machine Learning Physics Graduate Intern WinterSpring 2026

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.

Qualifications :

• Continuing student in good standing at an accredited institution of higher education pursuing a graduate degree in Physics or related field.
• Research background with a record of publication.
• Experience in writing codes (in C/C and Python) and a background in Materials Science/Engineering/Physics/Applied Mathematics.
• Excellent skills in written and verbal communication as well as teamwork.

Qualifications We Desire

• Experience in parallel computing porting codes to GPUs experience in numerical solutions of partial differential equations.

Pay Range

$6752 - $8201 Monthly

This position is under a step structure. Please note that the step placement is determined by your most recent completed academic year. 

Additional Information :

#LI-Onsite

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  Remote Work :

  No

  
  

  Employment Type :

  Full-time