RAP opportunity at National Security Agency NSA
Theoretical studies of quantum materials using density functional theory-based methods and machine learning
Location
National Security Agency, Laboratory for Physical Sciences
| opportunity |
location |
|
| 36.01.00.C1171 |
College Park, MD 20740 |
Advisers
| name |
email |
phone |
|
| Pratibha Dev |
pdev@lps.umd.edu |
301 935 6437 |
Description
The Materials Simulation Group (MSG) at the Laboratory for Physical Sciences (LPS) is seeking a postdoctoral associate to perform research in quantum materials using ab intio methods and machine learning. Our research spans different classes of novel quantum materials, including layered materials and their heterostructures, topological materials, altermagnets, and superconductors, for their potential applications in current and emerging technologies. In many of these applications, defects play an important role. Whether introduced intentionally or present inadvertently, they profoundly change material properties. Hence, studying defect properties is a major research thrust in our group. Equally central to our research are quantum-mechanical studies of 2D materials and their heterostructures (with or without a twist), where the physics of the composite is dictated by rich emergent phenomena. We work closely with experimentalists performing world-class research at LPS, along with the researchers at a number of national laboratories and universities.
Eligible candidates with experience using density functional theory-based methods and machine learning to investigate the aforementioned class of materials and different phenomena are encouraged to apply. Successful applicants will have a PhD. in computational/theoretical condensed matter physics or related disciplines, with a proven capacity for excellent research that is reflected in a strong publication record. A strong background in electronic structure methods is essential. Preference will be given to candidates with prior experience in studying defect properties, layered 2D materials, topological, and/or superconducting materials. Experience with machine learning is highly desirable.
References
1) S. Debata, S. K. Narayanan, and Pratibha Dev, “ Hidden in Plain Sight: Aromaticity of Hexagonal Boron Nitride.” Chemistry – A European Journal (2026): e71707.
https://doi.org/10.1002/chem.71707
2) Sajjan Sheoran and Pratibha Dev, “Tuning spin currents in collinear antiferromagnets and altermagnets," Physical Review B, 113, 174426 (2026).
https://link.aps.org/doi/10.1103/159t-fq4k
3) Samuel G Carter, Infiter Tathfif, Charity Burgess, Brenda VanMil, Suryakanti Debata, Pratibha Dev, “Influence of nitrogen doping and annealing on the silicon vacancy in 4H-SiC." Physical Review B, 112, 085209 (2025).
https://link.aps.org/doi/10.1103/ld1v-b696
Keywords
Two-dimensional materials; emergent phenomenon; defects for quantum sensing; altermagnetism; superconductivity; ab-initio methods; machine learning
Eligibility
citizenship
Open to U.S. citizens, permanent residents and non-U.S. citizens
level
Open to Postdoctoral and Senior applicants
Stipend
| Base Stipend |
Travel Allotment |
Supplementation |
|
| $101,401.00 |
$6,000.00 |
|
|
Additional Benefits
relocation
Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.
health insurance
A group health insurance program is available to awardees and their qualifying dependents in the United States.