Lawrence Livermore National Laboratory (LLNL) is seeking Machine Learning Graduate Student Interns to engage in practical research experience to further their educational goals. The interns will work on multidisciplinary projects focused on developing classical empirical and machine learning interatomic potentials, discovering and solving partial differential equations (PDEs) to model material behavior at the continuum scale, and analyzing large atomic datasets.
Requirements
- Experience in writing codes (in C/C++ and Python)
- Experience in parallel computing
- porting codes to GPUs
- experience in numerical solutions of partial differential equations.
Responsibilities
- 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.
Other
- 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.
- background in Materials Science/Engineering/Physics/Applied Mathematics.
- Excellent skills in written and verbal communication, as well as teamwork.
- Security Clearance: None required. However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.
