Postdoc AI Researcher for Grid Foundations & Power Systems

Brookhaven National Laboratory; New York (NY); On-site; USD 70,200 - 85,000; Full time

Job summary

Brookhaven National Laboratory invites applications for a Postdoctoral AI Researcher in Power Systems, based in Upton, NY. The role focuses on AI-enabled workflows for electric power grids and the GridFM foundation model, with emphasis on distribution networks and large-scale electrification.

The successful candidate will extend GridFM capabilities, develop graph-based ML models, generate data, and benchmark them.

Qualifications

• PhD in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field.
• Strong background in machine learning and deep learning.
• Experience with PyTorch, JAX, TensorFlow, or similar frameworks.
• Some experience developing Graph Neural Networks (GNNs) 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/TF

Graph neural networks

Uncertainty quantification

GPU computing

Independent research

Education

PhD in CS/EE/Math/Physics

Tools

PowerModels

MATPOWER

PSS/E

GridLAB-D

OpenDSS

Docker

Git

Job description

Brookhaven National Laboratory invites applications for a Postdoctoral AI Researcher in Power Systems, based in Upton, NY. The role focuses on AI-enabled workflows for electric power grids and the GridFM foundation model, with emphasis on distribution networks and large-scale electrification.

The successful candidate will extend GridFM capabilities, develop graph-based ML models, generate data, and benchmark them.