Postdoctoral AI Researcher in Power Systems

The Energy and Photon Science Directorate advances basic science that underpins discoveries and breakthroughs for energy systems. The appointment will be for a one-year with an opportunity for a one-year renewal to perform research in the area of electric power grids based 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. In particular, the position focuses on advancing GridFM, a grid foundation model for power systems. The role 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. Essential Duties and 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 Required Knowledge, Skills, and Abilities: 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 Knowledge, Skills, and Abilities: 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. BNL policy requires that after obtaining a PhD, 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 and R&D position, excluding time associated with family planning, military service, illness or other life-changing events. Brookhaven Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $70,200 - $85,000/ year. You will be placed at the level and salary commensurate with your experience. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group. 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