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PhD Position: Physics-Informed Generative AI for Synthetic Energy Data

EURAXESS hosting organisation · عدة بلدان أوروبية

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 d

At a glance

Where: Multiple European countries
Deadline: 2026-09-30
Funding: EUR 1 (parsed from page — verify)
Board: EURAXESS

Who can apply

Criteria stated by the funder on the official source.

Nationality

EURAXESS vacancies rarely print a nationality table. Confirm whether applicants from Tunisia can apply and whether a residence/work permit pathway exists on the employer page.

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Education

Specific Requirements - You hold an MSc degree (or will obtain one before the starting date) in computer science, artificial intelligence, data science, applied mathematics, physics, electrical engineering, or a related field. - You have a solid background in machine learning; experience with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. - You have good programming skills in Python and experience with a deep learning framework such as PyTorch.

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Funding and what it does not cover

Funding details were taken from the official programme page text. Confirm the current call before applying.