Can you help unlock the data needed for the energy transition? In the NWO-funded SHARE project, you will develop AI models that generate realistic, privacy-preserving synthetic energy data for grid planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector.
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
Application deadline: 25 October 2026
Contact: Yuliya Shapovalova
Apply at: https://www.ru.nl/en/working-at/job-opportunities/phd-position-physics-informed-generative-ai-for-synthetic-energy-data