Brookhaven National Laboratory's Condensed Matter Physics and Materials Science Division is seeking to advance research on correlated and topological systems by developing and applying state-of-the-art machine learning and electronic structure techniques to investigate electron and spin systems.
Requirements
- Demonstrated expertise in machine learning techniques and proficiency in scientific programming (Python and/or Fortran/C++).
- Knowledge of Density Functional Theory calculations and Wannier method-based spin Hamiltonian downfolding techniques.
- Familiarity with high throughput computing using modern GPU hardware as well as other computational methods such as Monte-Carlo and mean-field calculations.
Responsibilities
- Perform research on correlated electron and spin systems using ab-initio, model Hamiltonian and machine learning-based methods.
- Publish high-quality papers and give external presentations on your work.
- Collaborate actively with various theoretical and experimental groups within CMPMSD.
Other
- Ph.D. in Theoretical Condensed Matter Physics, Chemistry, Materials Science, or related discipline.
- Strong publication record in peer-reviewed journals and presentations at professional conferences, or equivalent public research contributions (e.g., GitHub repositories).
- Brookhaven Laboratory requires all non-badged personnel including visitors to produce a REAL-ID or REAL-ID compliant documentation to access Brookhaven National Laboratory.
- BSA employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1A.
