PHD Position

PhD Position | Physics-Informed Generative AI for Synthetic Energy Data Nieuw Can you use your expertise in AI and machine learning to help accelerate the energy transition? As a PhD candidate at Radboud University, you will develop generative AI models that create realistic, privacy-preserving synthetic energy data while respecting the physical laws of electricity networks. Working with real-world data from Alliander and an interdisciplinary research team, you will contribute to innovative research and open-source tools that support better energy planning and grid management. PhD Position: Physics-Informed Generative AI for Synthetic Energy Data Faculty of Science - Application deadline: 25 October 2026 The Dutch energy transition depends on data that almost no one is allowed to see. Distribution system operators (DSOs), municipalities and energy communities need high-resolution grid and consumption data to plan grid reinforcements, heat networks and local flexibility, but privacy law (GDPR), commercial sensitivity and regulatory uncertainty keep this data locked away. Hence, critical infrastructure decisions are being made with incomplete information. Synthetic data offers a way out: realistic-but-artificial datasets that preserve the statistical, temporal and physical structure of real energy data without identifying real households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data that looks plausible but violates physics and is therefore of limited use for grid planning. As a PhD candidate you will develop physics-informed, domain-constrained generative models for energy-system data, the core scientific contribution of the SHARE project (Work Package 3). More concretely, your work will involve the following: - You will design and compare deep generative approaches, UAEs, GANs, diffus...