Dr Zarak Bhat joins Edinburgh AI team

Dr Zarak Bhat has taken a Postdoctoral Research Associate position at the University of Edinburgh’s APRIL AI Hub (April 2026), where she will work on applying artificial intelligence to electronic device design and semiconductor workflows.

The role sits at the intersection of applied machine learning and device engineering — a growing area where ML methods are used for device modelling, performance prediction, design-space exploration, and design automation. Bhat’s PhD (NIT Srinagar, Dec 2019–Nov 2025) focused on advanced semiconductor technologies; her doctoral work produced power Gallium Nitride (GaN) device models and aimed to connect device-level physics with circuit-level simulation, a technical bridge that benefits both device researchers and systems ML practitioners building hardware-aware models.

Bhat completed an MTech (Jamia Millia Islamia, 2017–2019) and a BE (University of Kashmir, 2012–2016), both with top grades. She is the first PhD graduate from NIT Srinagar’s Nanoelectronics Research and Development Group. Her research has appeared in international journals including IEEE Transactions on Electron Devices. In August 2025 she took part in the India Semiconductor Workforce Development Program (ISWDP) advanced training. At APRIL she is listed as a Research Associate in AI for Electron Device Design.

This hire signals continued consolidation of talent into cross-disciplinary AI-for-hardware groups in top UK universities. For ML engineers and researchers working on hardware-aware ML, device co-design, surrogate modelling, or automated device-parameter optimization, Bhat’s profile exemplifies the practical research pipeline from device physics to ML-enabled workflows. Her GaN modelling background is especially relevant to power-electronics co-design and to teams building high-fidelity simulators or learned surrogates for device behavior.

Publications or open-source tool releases from APRIL AI Hub that incorporate Bhat’s device-modeling work; collaborations between APRIL and semiconductor industry partners; follow-up papers that apply ML methods to GaN device optimization or to end-to-end device-to-circuit simulation stacks.