PhD position on Adaptive Privacy and Differential Privacy for Energy Data

Offer Description

Research challenges

As a PhD candidate, you will develop methods for adaptive privacy protection of energy data, combining differential privacy, empirical privacy attacks and privacy–utility optimisation. Your research is part of the Adaptive Privacy and Legal Compliance work package in the SHARE project. More specifically, your challenges are to:

• Investigate how temporal patterns, spatial information, rare events and other distinctive features contribute to re-identification and inference risk. You will work with metrics such as uniqueness, entropy, attack success rates and time-series-specific indicators.
• Implement and study attacks such as membership inference, reconstruction, re-identification and attribute inference to understand where information leakage occurs in both original and synthetic datasets.
• Investigate methods that allocate stronger protection to sensitive components and lighter perturbation to lower-risk components, thereby preserving as much analytical utility as possible.
• Formulate privacy protection as an optimisation problem balancing formal privacy guarantees against statistical, temporal and physical utility requirements for energy-system applications.
• Collaborate closely with researchers at Radboud University, who develop physics-informed generative models for synthetic energy data, and investigate approaches such as differentially private training and post-generation privacy calibration.
• Work closely with distribution system operator Alliander and other consortium partners to evaluate whether the developed methods provide meaningful protection while retaining the information required for practical energy-system applications.
• Ultimately contribute to an open-source privacy layer for the SHARE toolbox.

Requirements

Requirements

You hold, or will soon obtain, an MSc degree in Computer Science, Cybersecurity, Artificial Intelligence, Data Science, Applied Mathematics, or a closely related discipline. You should have:

• a strong background in privacy-enhancing technologies, artificial intelligence, cybersecurity or statistical modelling;
• strong programming skills, particularly in Python;
• preferably, some familiarity with deep generative models (VAEs, GANs, diffusion models) and probabilistic modelling;
• good written and spoken English skills;
• knowledge of Dutch language preferred not mandatory.

Additional Information

Benefits

The PhD candidate will be appointed for a period of 15 months. The appointment will be extended to 4 years when progress and performance are good. A PhD training program is part of the agreement.

Salary

The salary is determined in accordance with salary scale P of Appendix A of the Collective Labour Agreement of Dutch Universities and ranges from € 3.204,-- gross per month upon commencement to € 4.051,-- gross per month in the fourth and final year, in case of full employment.

The Open Universiteit provides good secondary benefits such as training, mobility, part-time employment and paid parental leave.

Station

The position is officially based in Heerlen. As part of the PhD project, you will mainly work at Radboud University in Nijmegen, where you will collaborate closely with the other researchers in the SHARE project. This setup enables close day-to-day collaboration with researchers working on machine learning, synthetic data and energy systems.

You will be employed by Open Universiteit and supervised by Dr. Mina Alishahi as your primary supervisor, with Prof. dr. ir. Harald Vranken as co-supervisor.

Selection process

Please apply to this position by sending an email to solliciteren@ou.nl, stating the vacancy number and including:

• your CV;
• a motivation letter;
• your MSc thesis or another representative piece of academic work, if available;
• contact details of two references.

In your motivation letter, please briefly address the following three questions:

1. Why are you interested in privacy-preserving machine learning and, in particular, this PhD project?
2. Describe a research, thesis or programming project in which you worked with machine learning, privacy, cybersecurity, generative models or complex data. What was your own contribution?
3. Provide a one-paragraph description of your MSc thesis, written for a reader outside your field.

You will preferably start your employment on January 1st, 2027.

Closing date: November 1st, 2026.

Information

For more information about this vacancy you can contact: harald.vranken@ou.nl.

Work Location(s)

• Number of offers available: 1
• Company/Institute: Open Universiteit
• Country: Netherlands
• City: Heerlen
• Postal Code: 6401 DL
• Street: Postbus 2960

Contact

• City: Heerlen
• Website: http://www.ou.nl/
• Street: Valkenburgerweg 177
• Postal Code: 6419 AT