Postdoc: Real-Space Electronic Structure on GPUs

UT-Battelle; Oak Ridge (TN); On-site; USD 65,000 - 90,000; Full time

Job summary

UT-Battelle at Oak Ridge National Laboratory invites applications for a Postdoctoral Researcher in the Advanced Computing for Chemistry and Materials group. The role focuses on Real-Space Electronic Structure methods, large-scale many-body calculations, and GPU-accelerated algorithms to advance materials science and condensed matter physics.

The candidate will develop, optimize, and benchmark scalable codes for HPC platforms, collaborating across disciplines and contributing to peer‑reviewed

Qualifications

• PhD in a relevant field completed within the last 5 years.
• Expertise in parallel application development techniques (parallel programming models, GPU Programming, algorithms, and software) and application of electronic structure calculations for materials physics.
• Excellent communication and collaboration skills.

Responsibilities

• Collaborate in a multi-disciplinary research environment supporting the Laboratory’s missions.
• Debug, profile, and tune materials applications for high performance on OLCF systems.
• Adapt, develop, and publish scalable applications and algorithms for OLCF computers.
• Apply and improve computational methodologies to optimize materials for scientific objectives.
• Publish papers and present findings in technical forums.

Skills

GPU programming

MPI

OpenMP

CUDA

HIP

Real-space electronic structure

Parallel algorithms

C/C++

Education

PhD in Engineering, Computer Science, Condensed Matter Physics, Material Sciences, Chemistry, Applied Mathematics or related field

Tools

CUDA

HIP

Job description

UT-Battelle at Oak Ridge National Laboratory invites applications for a Postdoctoral Researcher in the Advanced Computing for Chemistry and Materials group. The role focuses on Real-Space Electronic Structure methods, large-scale many-body calculations, and GPU-accelerated algorithms to advance materials science and condensed matter physics.

The candidate will develop, optimize, and benchmark scalable codes for HPC platforms, collaborating across disciplines and contributing to peer‑reviewed