Postdoc: AI Grid Foundation Models for Power Systems

Brookhaven Science Associates, LLC; Upton (NY); On-site; USD 70,200 - 85,000; Full time

Job summary

Brookhaven Science Associates, LLC invites applications for a one‑year term in the Energy and Photon Science Directorate to advance AI foundation models for electric applications focusing on GridFM. The role emphasizes developing scalable graph ML approaches, data generation, and rigorous benchmarking.

The selected candidate will work on GPU‑accelerated training, uncertainty quantification, and physics‑informed AI, with opportunities to extend the work through a renewal.

Qualifications

• Ph.D. required in a relevant field with demonstrated research capability.
• Strong background in machine learning and deep learning.
• Experience with PyTorch, JAX, TensorFlow, or similar frameworks.
• Some experience developing Graph Neural Networks (GNNs), Graph Transformers, or foundation‑model architectures.
• Familiarity with model training, fine‑tuning, evaluation, and deployment.
• Understanding of uncertainty quantification, model robustness, and physics‑informed AI.
• Experience with GPU computing and large‑scale model training.
• Demonstrated ability to conduct independent research.

Responsibilities

• Extend current GridFM capabilities for distribution networks.
• Develop scalable graph‑based machine learning or related models.
• Expand training data generation capabilities.
• Create benchmarks and test developed models.

Skills

Machine learning

Deep learning

PyTorch

JAX

TensorFlow

GNNs / Graph Transformers

Uncertainty quantification

Physics-informed AI

GPU computing

Independent research

Education

Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field

Tools

Git

Docker

CI/CD

OpenDSS

GridLAB‑D

MATPOWER

PowerModels

PSS/E

Job description

Brookhaven Science Associates, LLC invites applications for a one‑year term in the Energy and Photon Science Directorate to advance AI foundation models for electric applications focusing on GridFM. The role emphasizes developing scalable graph ML approaches, data generation, and rigorous benchmarking.

The selected candidate will work on GPU‑accelerated training, uncertainty quantification, and physics‑informed AI, with opportunities to extend the work through a renewal.