Machine Learning Physics Graduate Student

About the role

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.

Responsibilities

• Develop parallel C/C++/Python codes to train, test and evolve (a) PDEs (for phase field and phase field crystal models) discovered from data, and (b) interatomic potentials developed from quantum simulations.
• Explore the use of machine learning methods to discover and evolve PDEs for phase field and phase field crystal models.
• 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.

Requirements

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA).
• 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

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

Pay

Pay Range: $6,752 - $8,201 Monthly
This position is under a step structure. Please note that the step placement is determined by your most recent completed academic year.

Schedule

This position requires full-time on-site presence due to the nature of the work.