Postdoctoral Research Associate - AI Models Power Grid System

Overview

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation's most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals. Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment.

These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED) seeks a Postdoctoral Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large‑scale electric power system simulations on DOE leadership‑class computing resources.

• Participate in the design, implementation, and deployment of scalable AI/DL models for power grid systems, including surrogate models and foundation‑model workflows for OPF and related grid simulation tasks.
• Develop and maintain HPC‑ready software workflows for distributed training, large‑scale inference, scalable data ingestion, and data management on leadership‑class computing systems and institutional clusters.
• Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi‑node GPU/CPU workflows, monitoring, restart, and post‑processing pipelines.
• Author peer reviewed papers for journals and conferences, technical reports, open‑source software, and represent the organization by making technical presentations at workshops and conferences.
• Collaborate within a multi‑disciplinary research environment consisting of computational scientists, computer scientists, electrical engineers, domain scientists, and applied mathematicians conducting basic and applied AI/DL research in support of the Laboratory's missions.

• A PhD in computer science or an AI‑related field completed within the last 5 years.
• Demonstrated expertise in scalable deep learning, including distributed training and/or large‑scale inference using modern AI frameworks such as PyTorch.
• Demonstrated experience with high‑performance computing systems, including multi‑node workflows on CPU and/or GPU clusters.
• Demonstrated expertise in scalable data management for AI/ML workflows, including efficient data preprocessing, storage, streaming, and I/O for large scientific datasets.
• Demonstrated experience writing SLURM and PBS job submission scripts for HPC clusters, including batch workflows, job arrays, environment setup, and restart logic.
• Demonstrated expertise with the Linux operating system, bash scripting, Git, Python, and reproducible software environments.
• Demonstrated expertise in writing advanced software in Python and in the design and implementation of deep learning algorithms.
• Expertise in object‑oriented programming, scripting languages, and modern software engineering practices for research codes.
• Demonstrated effective written and oral communication skills, a proven publication record, and effective interpersonal skills.

• Knowledge of graph neural networks and other geometric deep learning approaches for graph‑structured scientific or engineering data.
• Background in electrical engineering, power systems, grid modeling, or power system optimization.
• Experience with optimal power flow and grid simulation solvers or tool chains such as MATPOWER, PSS/E, Power Models, or related open‑source or commercial packages.
• Experience working in a multi‑disciplinary research environment that follows modern software quality standards, including version control, unit testing, documentation, and continuous integration.
• Motivated self‑starter with the ability to work independently, participate creatively in collaborative teams, function well in a…