GEM- Graduate Research Associate, Post Masters- Power Systems

The SRNL GEM Doctoral Research Fellow in Power Systems will contribute to SRNL’s mission of advancing the resilience and intelligence of the nation’s energy infrastructure by conducting multidisciplinary research at the intersection of power systems, physics-informed digital twins, and AI/ML. The Fellow will join a strategic workforce-development pipeline designed to address the critical national shortage of U.S. PhD scientists with expertise in dynamic and transient analysis of advanced energy systems.

The Fellow will work alongside SRNL scientists and university faculty on energy-systems research topics encompassing dynamic and transient analysis of electrical, thermal, and mechanical energy systems; real-time simulation using Controller Hardware-in-the-Loop (CHIL) and Power Hardware-in-the-Loop (PHIL); and the development of physics-informed AI/ML methods and digital-twin capabilities for nuclear-SMR, grid, and renewables applications.

Fellows are provided an immersive learning environment to develop into the next generation of multidisciplinary energy researchers, integrate academic coursework with hands-on national-laboratory R&D, and make impactful contributions to ongoing DOE programs.

Work scope may include contributions to the design and operation of CHIL/PHIL testbeds; development and validation of physics-informed machine-learning models; AI-driven digital-twin construction for cyber-physical energy systems; data-driven analysis of phasor measurement, EMS, and operational utility data; and participation in publications and conference presentations in collaboration with SRNL principal investigators and university advisors.

Successful candidates will:

• Conduct original research in physics-informed digital twins, AI/ML methods, and real-time simulation of energy systems
• Design, execute, and document experiments using CHIL/PHIL hardware-in-the-loop testbeds at SRNL’s Advanced Manufacturing Collaborative (AMC)
• Think critically, analyze data, and integrate these skills into effective experimental design, execution, and reporting
• Develop partnerships with internal team members of different backgrounds across electrical, mechanical, applied math, and physics disciplines
• Effectively communicate (verbal and written) technical results, including peer-reviewed publications and conference presentations
• Maintain strong academic standing while balancing SRNL research responsibilities with PhD coursework and dissertation milestones
• Work safely with particular attention to quality. Comply with all SRNL, university, and DOE policies and standards

Minimum Qualifications:

• For ability to obtain and maintain a security clearance, US Citizenship is Legally Required

• Must be a U.S. Citizen

• Bachelor’s or Master’s degree (recently earned or expected within the next term) in Electrical & Computer Engineering, Mechanical Engineering, Applied Mathematics, Physics, or a closely related discipline

• Admitted to, or in the process of being admitted to, an accredited PhD program in a relevant discipline

• Minimum cumulative GPA of 3.6 or higher; transcript required prior to hiring

• Demonstrated research interest in power and energy systems, physics-informed digital twins, and/or AI/ML methods

Preferred Qualifications:

• Admission to a PhD program at a BSRA-affiliated university (Clemson University, Georgia Institute of Technology, University of Georgia, University of South Carolina, or South Carolina State University) or at a university with an existing SRNL scientist appointment (Florida State University, Virginia Tech) is preferred; candidates from other accredited engineering PhD programs will also be considered

• Coursework or hands-on research experience in dynamic and transient analysis of electrical, thermal, or mechanical energy systems

• Experience with real-time simulation platforms (e.g., RTDS, OPAL-RT, Typhoon HIL) and CHIL/PHIL testbeds

• Familiarity with physics-informed machine learning, digital-twin development, or scientific computing frameworks

• Faculty recommendation from the candidate’s prospective PhD advisor

• Prior conference presentations, publications, or participation in student research teams

Work Schedule and Location

•  Location: SRNL Advanced Manufacturing Collaborative (AMC) facility

• Summer: Full-time (40 hours/week) on-site at SRNL

• Academic Year: Once the candidate has met their PhD program’s residency requirements at their affiliated university, 20 hours/week at SRNL alongside coursework and dissertation research

• Durati