Postdoctoral Fellow in Power System Economics

Position Description:

Professors Le Xie (SEAS) and James Stock (HKS) seek a motivated postdoctoral fellow to conduct interdisciplinary research at the intersection of artificial intelligence, power system engineering, and energy economics, with a focus on accelerating energy system transformation. The position will be housed in the Salata Institute for Climate and Sustainability as part of a broader research initiative on enabling the transition to a deeply decarbonized and reliable energy system.

The Postdoctoral Fellow will work closely with the project directors and collaborators to develop data-driven and economically grounded frameworks for understanding how AI-enabled control, optimization, and market design can support large-scale decarbonization, grid modernization, and the integration of distributed and flexible energy resources.

Research topics may include—but are not limited to—AI-based grid operation and planning, reinforcement learning for distributed system coordination, electricity market design, pricing mechanisms for reliability and flexibility, investment and planning under uncertainty, and the macroeconomic and policy implications of energy system transformation.

Fellows will engage in rigorous empirical, computational, and theoretical research that integrates engineering models of power systems with modern econometric and economic analysis. Responsibilities include conceptualizing and implementing statistical and structural models, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis, programming following best practices for reproducible research, presenting findings to academic and policy audiences, and co-authoring high-impact publications.

The Fellow will contribute to collaborative research across SEAS and HKS while also pursuing independent but complementary work that advances the understanding of how AI and economic design can jointly enable reliable, efficient, and equitable energy system transformation.

Basic Qualifications:

Ph.D. in electrical engineering, applied mathematics, economics, operations research or a related field.

Additional Qualifications:

Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and/or adaptive control) with a strong desire to make an impact on energy/power grids are preferred. Candidate should have substantial publication history, in addition to good teamwork and communication skills.