Brookhaven National Laboratory logo

Postdoctoral AI Researcher in Power Systems

Brookhaven National Laboratory

UPTON, NYResearch$70–85K/yrSeen todaySeen in employer's feed today

Most applications go out cold — see where you stand first. No sign-up to start.

Watch jobs like this.

At a glance

Compensation
$70–85K/yr
Location
UPTON, NY
Role Type
Research
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Requirements

Credentials this posting asks for.

Doctorate

Job overview

The postdoctoral researcher will conduct research on electric power grids, contributing to GridFM and AI-enabled workflows for electric applications. The work focuses on extending the model for distribution networks, developing scalable graph-based machine learning, expanding training-data generation, and creating benchmarks. The one-year appointment may be renewed for one additional year, based on funding and individual performance, and supports translating AI research into utility applications.

Skills & qualifications

RequiredNice to have

Skills

Machine LearningDeep LearningPyTorchJAXTensorFlowGraph Neural NetworksGraph TransformersFoundation ModelsModel TrainingFine-TuningModel EvaluationModel DeploymentUncertainty QuantificationModel RobustnessPhysics-Informed AIGPU ComputingLarge-Scale Model TrainingIndependent ResearchDistributed ComputingHPC EnvironmentsCloud PlatformsProduction SoftwareML PipelinesGitCI/CDContainerizationDockerReproducible WorkflowsAPI DevelopmentWorkflow OrchestrationAI Workload OptimizationElectric Power SystemsTransmission NetworksDistribution NetworksPower FlowOptimal Power FlowContingency AnalysisGrid PlanningPowerModelsMATPOWER

Qualifications

PhD in Related FieldCompleted Degree Requirements by StartFive Years Post-PhD R&D Experience Maximum

Full job description

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 modelsRequired 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 withPyTorch, JAX, TensorFlow, or similar frameworks. * Someexperience 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. * ExperienceoptimizingAI 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 withGurobi, CPLEX,Pyomo,JuMP, or related tools. * Experience with LLM-based workflows, tool-calling agents, MCParchitectures, 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. Brookhaven National Laboratory is committed to employee success and we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Review more information at BNL | Benefits Program Brookhaven National Laboratory requires all non-badged personnel including visitors to produce

Similar jobs, posted recently

Open roles like this one, listed in the last 30 days.

You've read the whole posting — now see how you match it.