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

EPRI•Knoxville, TN

56d•$16 - $36•Onsite

About The Position

This is an internship position for a student to support R&D projects related to AI-driven power system modeling, including power flow, state estimation, and large-scale grid analytics under high renewable penetration. Looking for students who can work at minimum in the 2026 Fall semester (August-December).

Requirements

Minimum 1 year of Master’s or PhD (in Electrical Engineering focusing on 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)
Experience with data processing, numerical computing, and model development
Strong technical writing and presentation skills

Nice To Haves

Electrical engineering PhD student with emphasis on AI for power systems
Exposure to graph neural networks will be considered a plus

Responsibilities

Basic 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)