Postdoctoral AI Researcher in Power Systems
One-year appointment with an opportunity for a one-year renewal to perform research in electric power grids, contingent on funding and individual performance. The successful candidate will contribute to the development of next-generation AI foundation models and AI-enabled workflows for electric applications, focusing on advancing GridFM, a grid foundation model for power systems.
About the role
The position offers unique opportunities to contribute to cutting-edge research while helping translate AI innovations into real-world utility applications that support grid modernization, resilience, and large-scale electrification.
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
Requirements
- Ph.D. 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), 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
Preferred Qualifications
- Familiarity with distributed computing, HPC environments, and cloud platforms
- Experience building production-quality software and ML pipelines
- Familiarity with Git, CI/CD, containerization (Docker), and reproducible workflows
- Experience developing APIs and workflow orchestration systems
- Experience optimizing AI workloads for performance and scalability
- Basic knowledge of electric power systems, transmission/distribution networks, power flow, optimal power flow, contingency analysis, or grid planning
- Familiarity with tools such as PowerModels, MATPOWER, PSS/E, GridLAB-D, OpenDSS, or similar
- Experience with mathematical optimization, mixed-integer programming, stochastic optimization, or decision analytics
- Familiarity with Gurobi, CPLEX, Pyomo, JuMP, or related tools
- Experience with LLM-based workflows, tool-calling agents, MCP architectures, retrieval systems, or AI copilots
- Familiarity with multi-agent systems and decision-support applications
Other Information
- Candidates must have completed all degree requirements by the commencement of employment
- After obtaining a Ph.D., eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness, or other life-changing events
- Brookhaven National Laboratory requires all non-badged personnel, including visitors, to produce a REAL-ID or REAL-ID compliant documentation to access the site
- As a U.S. Department of Energy laboratory, Brookhaven National Laboratory requires employees to obtain and maintain a DOE Uncleared Personal Identity Verification (UPIV) credential
Pay
The full salary range for this position is $70,200 - $85,000 per year. Salary offers will be commensurate with the final candidate’s qualifications, education, and experience.
Benefits
Brookhaven National Laboratory offers a comprehensive employee benefits program. Review more information at BNL | Benefits Program.