Keith Moffat

I develop data-driven control and optimization methods for power system applications. This includes stabilizing electric grids powered entirely by wind, solar, and batteries, turning data centers into grid-stabilizing assets, and developing provably-safe ways to use AI for grid operation.

My expertise spans control, optimization, and system identification, and I apply it to power systems applications including data-driven grid stability, adaptive grid optimization, and electricity market design.

I am recruiting PhD students with demonstrated mathematical expertise and experience with electrical power systems, control theory, system identification, and/or optimization theory. In addition, curiosity, the ability to work independently, and to collaborate, are critical.

I currently have open postings for the following PhD positions

Applied optimization theory for online grid operation
Robust/nonlinear data-driven predictive control

If you fit the description above and would like to apply, please send me an email with your CV and transcript with “applied optimization PhD application” or “Robust/nonlinear DPC” in the title. Please note that I receive many emails and will not be able to respond to all applications.

For talks you may copy-and-paste the third-person bio below

Keith Moffat (Member, IEEE) received the B.E. degree from Dartmouth College, USA, in 2016, and the Ph.D. degree in Electrical Engineering and Computer Sciences from the University of California, Berkeley, USA, in 2022. From 2022 to 2025, he was a Postdoctoral Researcher with the Automatic Control Laboratory (IfA), ETH Zürich, Switzerland. Since September 2025, he has been a Senior Lecturer (~Assistant Professor) at the University of Melbourne, Australia. His research interests include power systems, power electronics, control theory, system identification, and optimization.