Machine Learning Physics Graduate Student

Company Description

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

Job Description

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.

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

You will

• 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.

Qualifications

• 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 We Desire

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