Machine Learning Physics Graduate Student

Lawrence Livermore National Laboratory (LLNL) is a leader in advancing science and technology for national security and global stability. They are seeking Machine Learning Graduate Student Interns to engage in practical research, focusing on the development of interatomic potentials and numerical solutions of partial differential equations.

What you'll do:

• 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

What they're looking for:

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

Benefits:

• Holiday Pay
• Sick leave accrual
• Individual 401(k) contributions