PhD Position: Physics-Informed Generative AI for Synthetic Energy Data at Radboud University, Netherlands
Radboud University
PhD: create physics-informed generative AI for privacy-preserving synthetic energy grid time series (diffusion/GAN/VAE, GNNs). Python/PyTorch; 10% teaching.
PhD: create physics-informed generative AI for privacy-preserving synthetic energy grid time series (diffusion/GAN/VAE, GNNs). Python/PyTorch; 10% teaching.
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Recorded source check: September 21, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
€ 3,204 - € 4,051 Required background Research University Degree Organizational unit Faculty of Science Application deadline 25 October 2026 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, yo
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