PhD: Physics-Informed Generative AI for Energy Data

Radboud University; Netherlands; On-site; EUR 38,000 - 49,000; Full time

Benefits offered by this job

Holiday allowance 8%

End-of-year bonus 8.3%

Extra leave days 30 or 41

Job summary

Radboud University is seeking a PhD candidate to develop physics-informed generative models for energy-system data, using real-world data from Alliander and collaborating with privacy researchers. You will publish at top ML venues and contribute to an open-source toolbox used by DSOs and researchers in the Netherlands.

The role involves close collaboration with other researchers, on-site work in Nijmegen, and a focus on machine learning methods including VAEs, GANs, diffusion models, and

Qualifications

• MSc degree in computer science, artificial intelligence, data science, applied mathematics, physics, electrical engineering, or related field.
• Strong background in machine learning; deep generative models or probabilistic modeling is a plus.
• Proficient Python programming and PyTorch experience.
• Enjoys interdisciplinary work with privacy researchers and energy practitioners.
• Good command of spoken and written English.

Responsibilities

• Develop physics-informed, domain-constrained generative models for energy-system data.
• Compare deep generative approaches (UAEs, GANs, diffusion models, flow matching) and GP methods.
• Embed physical constraints like power-flow consistency and network topology.
• Publish results at leading ML venues and build open-source tools.
• Collaborate with PhD candidate and postdoctoral researcher on privacy integration.

Skills

Python

PyTorch

English

Interdisciplinary collaboration

Education

MSc degree (CS/AI/DS/Math/Physics/EE)

Tools

PyTorch

Job description

Radboud University is seeking a PhD candidate to develop physics-informed generative models for energy-system data, using real-world data from Alliander and collaborating with privacy researchers. You will publish at top ML venues and contribute to an open-source toolbox used by DSOs and researchers in the Netherlands.

The role involves close collaboration with other researchers, on-site work in Nijmegen, and a focus on machine learning methods including VAEs, GANs, diffusion models, and