Van-Hai Bui

PhD position available at the University of Michigan-Dearborn in the Intelligent Energy Systems Lab with Assistant Professor Van-Hai Bui . The position is for a motivated PhD student to begin in Winter 2027 (January 2027), with the post also referencing a Fall 2027 timing in the headline. The research is centered on Electrical Engineering and Computer Science topics, especially artificial intelligence , machine learning , optimization , and modern power and energy systems . Research areas include AI/ML for power systems, renewable energy integration, grid modernization, electric vehicles, vehicle-to-grid (V2G) and vehicle-to-vehicle (V2V) technologies, microgrids, distributed energy resources, and power system reliability, stability, and resilience analysis. The group’s work sits at the intersection of cyber-physical energy infrastructure, optimization, and data-driven energy management. The post says students will have opportunities to work on cutting-edge projects supported by federal agencies and industry partners. Applicants with either a Master’s degree or a Bachelor’s degree seeking direct PhD admission are encouraged to apply. Application window: interviews are expected during June–July 2026 , and the application deadline is end of July 2026 (normalized here as 2026-07-31). How to apply: send your CV and relevant supporting materials to [email protected] . For more information, contact the professor directly.

NSF-funded research announcement from the University of Michigan-Dearborn in the area of Electrical Engineering and Computer Science . Assistant Professor Van-Hai Bui announces that his NSF project, “ERI: Scalable Machine Learning Frameworks for Stability Enhancement in Inverter-Dominated Power Systems,” has been awarded for the 2026–2028 period. The project is supported through the NSF Engineering Research Initiation (ERI) program. The research focuses on modern power systems that increasingly depend on inverter-based resources such as distributed energy generation and battery energy storage systems. These systems are essential for a sustainable energy future, but they also create fast and complex dynamics that make monitoring and control more difficult. The project aims to develop intelligent, data-driven methods that can estimate system behavior in real time and recommend actions to prevent instability. Research themes include power systems stability , machine learning , inverter-dominated grids , energy storage systems , distributed generation , real-time estimation , and privacy-preserving solutions . The post thanks NSF, collaborators, students, mentors, and the University of Michigan-Dearborn for support. This is a research funding announcement, not a PhD, postdoc, or master’s application call. No deadline, application portal, or student recruitment details are provided.