PhD in Physics-Informed Generative AI for Energy Data

Radboud Universiteit; Nijmegen; On-site; EUR 36,000 - 45,000; Full time

Benefits offered by this job

Holiday allowance (8%)

End-of-year bonus (8.3%)

Pension plan

Flexible leave & work hours

Job summary

Radboud University Nijmegen invites applications for a PhD position focused on Physics-Informed Generative AI for Synthetic Energy Data. You will develop physics-constrained generative models for energy-system time series and publish in leading ML venues, using real data from Alliander and contributing to an open-source toolbox.

You will collaborate with privacy researchers, legal scholars and energy practitioners, and spend up to 10% of time on teaching activities within computing science

Qualifications

• MSc degree (or will obtain one before start) in CS/AI/Data Science/Applied Math/Physics/EE or related field.
• Strong background in machine learning; experience with deep generative models (VAEs, GANs, diffusion) or probabilistic modelling is a plus.
• Good programming skills in Python and experience with a deep learning framework such as PyTorch.
• Interdisciplinary work with privacy researchers, legal scholars and energy-sector practitioners.
• Good command of spoken and written English.

Responsibilities

• Design and compare deep generative approaches, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series.
• Embed physical constraints into generation: power-flow consistency, operational bounds, and network topology through graph neural networks.
• Build validated benchmark datasets and an evaluation framework covering fidelity, temporal and spatial structure, plausibility and downstream task performance.
• Collaborate with a fellow PhD candidate and postdoctoral researcher on integrating differential privacy into the generative pipeline.
• Contribute to an open-source synthetic data toolbox for use by DSOs, municipalities and researchers in the Netherlands.

Skills

Python programming

PyTorch

Machine learning background

Generative models

Interdisciplinary collaboration

English proficiency

Education

MSc degree in CS/AI/Data Science/Applied Math/Physics/EE

Tools

Python

Graph Neural Networks

Job description

Radboud University Nijmegen invites applications for a PhD position focused on Physics-Informed Generative AI for Synthetic Energy Data. You will develop physics-constrained generative models for energy-system time series and publish in leading ML venues, using real data from Alliander and contributing to an open-source toolbox.

You will collaborate with privacy researchers, legal scholars and energy practitioners, and spend up to 10% of time on teaching activities within computing science