Project description
Official university vacancy imported from Radboud University. Details are presented for discovery and may change. Confirm the latest requirements and apply on the official source.
- You will design and compare deep generative approaches, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series.
- You will embed physical constraints into generation: power-flow consistency (Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures.
- You will build validated benchmark datasets and an evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting).
- You will collaborate with a fellow PhD candidate and a postdoctoral researcher on integrating differential privacy into the generative pipeline, balancing privacy guarantees against data utility.
- You will contribute to an open-source synthetic data toolbox that DSOs, municipalities and researchers across the Netherlands will actually use.
Funding and compensation
Paid position. See the original posting for amounts, duration and conditions.
Eligibility and application requirements
Review the qualifications, research interests and required documents in the description above. The employer or university's original posting is the source for complete eligibility requirements.
How to apply
Submit your application through Radboud University's official application process before 25 Oct 2026.
Source and listing information
This opportunity is published by Radboud University. Read the original official posting. Conditions and availability may change; confirm them with the organization.
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