AI/ML for Power System Analysis, Power Flow, and State Estimation Fall Student Engineer

Duties & Responsibilities

• Familiarity with integrating physical constraints (power flow equations, network limits) into data-driven models (physics-informed ML concepts)
• Understanding of representing power systems as graphs and applying graph-based learning methods (e.g., graph neural networks)
• Exposure to developing machine learning models (preferably deep learning) for power system applications
• Working knowledge of AC/DC power flow, state estimation, and grid modeling fundamentals
• Procedure of running power flow simulations using tools such as PSS®E, PSLF, Pandapower, or MATPOWER, and understanding system modeling workflows
• Procedure of generating datasets using simulation tools for varying load, generation, and contingency conditions (N-1, N-k)

Qualifications

• Minimum 1 year of Master’s or PhD (in Electrical Engineering focusing on Power systems)
• Ideal Candidate: Electrical engineering PhD student with emphasis on AI for power systems
• Strong understanding of power flow and/or state estimation methods
• Familiarity with power system simulation tools (preferably PSS®E, PSLF, Pandapower, or MATPOWER)
• Strong programming skills (preferably in Python, MATLAB is a plus)
• Experience with machine learning or deep learning frameworks (e.g., PyTorch or TensorFlow)
• Exposure to graph neural networks will be considered a plus
• Strong technical writing and presentation skills