Fully Funded PhD Positions in Power Systems, AI, and Electricity Markets at New Mexico State University
The Klipsch School of Electrical & Computer Engineering at New Mexico State University (NMSU) is inviting applications for fully funded PhD positions starting Fall 2026. The research group, led by PNM Endowed Chair Professor Fengyu W., focuses on cutting-edge topics at the intersection of power system operation, power system planning, electricity market design, and AI-driven optimization for energy systems. Students will have the opportunity to work on large-scale federally and state-funded projects, contributing to grid modernization and energy resilience.
Key research areas include optimization (convex, stochastic, large-scale), power system engineering, and advanced machine learning techniques such as deep learning and reinforcement learning. The program is ideal for candidates with a strong background in Electrical Engineering, Computer Science, or closely related fields. Applicants should possess a Master's degree and demonstrate expertise in at least one of the core research areas.
Successful candidates will receive a full Research Assistantship (RA), which covers tuition and provides a stipend. The group offers a collaborative environment with real-world impact, working on projects that shape the future of energy systems and electricity markets. International students are welcome to apply, though they must meet English proficiency and transcript requirements, and should be aware of potential travel restrictions based on their country of origin. Detailed information for international applicants, including visa and I-20 processes, is available on the NMSU graduate school website.
The application deadline is March 1, 2026. Interested candidates should email Professor Fengyu W. at [email protected] with their CV, academic transcripts, and a brief statement of research interests. Selected applicants will be invited to submit a formal application to NMSU. For more information, visit the graduate program details online.
Keywords: Power system operation, power system planning, electricity market design, AI-driven optimization, energy systems, optimization, power system engineering, machine learning, deep learning, reinforcement learning.
