Drimva

Hitachi

Research Scientist - AI for Power Electronics

Raleigh, North Carolina, United States · On-site · Full-time · Posted May 19, 2026

Education
Ph.D. degree in Electrical Engineering with a focus on power electronics, energy conversion, or a closely related field, with educational background spanning both Power Electronics and Computer Science/AI.
Visa sponsorship
Candidate must already have work authorization that would permit them to work for Hitachi Energy in the United States.

About the job

Conduct applied research and develop AI-enabled software tools to automate, optimize, and validate power electronics design for energy systems.

What you'll do

  • Develop and validate AI methods and software tools for power electronics design automation, performance prediction, design-space exploration, and system optimization.
  • Support component selection, reliability assessment, and scalability improvements; translate engineering challenges into practical software solutions.
  • Collaborate with research and industry partners, and share findings through publications, technical reports, and patent applications.

What they ask for

  • Ph.D. in Electrical Engineering focused on power electronics, energy conversion, or a closely related field, with education spanning Power Electronics and Computer Science/AI.
  • Experience in power electronics design, modeling, hardware systems, and simulation/verification.
  • Hands-on experience applying AI techniques to engineering problems; proficiency in Python or similar and familiarity with machine learning frameworks.
  • Research publications in recognized journals or conferences, effective English communication, and existing U.S. work authorization permitting employment at Hitachi Energy.

Nice to have

  • Familiarity with Matlab, Simulink, PSCAD, and PLECS.
  • Familiarity with hardware design workflows and controller design techniques.

Tools

Python, PyTorch, TensorFlow, Matlab, Simulink, PSCAD, PLECS

AI focus

Apply AI, optimization algorithms, and data-driven methods to automate power electronics design and improve design-space exploration, performance prediction, and system optimization.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)