Key Skills

Job Description

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 decisions are being made with incomplete information. Synthetic data offers a way out: realistic-but-artificial datasets that preserve the statistical, temporal and physical structure of real energy data without identifying real households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data that looks plausible but violates physics and is therefore of limited use for grid planning. As a PhD candidate you will develop physics-informed, domain-constrained generative models for energy-system data, the core scientific contribution of the SHARE project (Work Package 3). More concretely, your work will involve the following: 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. This is research with a direct route to impact: you will work with real operational data from Alliander, with regular on-site visits and direct access to the practitioners who will use your models for congestion forecasting, spatial energy planning and flexibility assessment. You will publish at top machine learning venues while producing open datasets and tools with tangible societal impact. You will be expected to spend a small part of your time (up to 10%) on teaching activities, such as assisting in courses of our computing science programmes. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate. Profile 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. You enjoy interdisciplinary work: you will interact with privacy researchers, legal scholars and energy-sector practitioners. You have a good command of spoken and written English. Prior knowledge of energy systems is not required, we and our consortium partners will provide the domain context. We offer We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract). You will receive a starting salary of €3,204 gross per month based on a 38-hour working week, which will increase to €4,051 in the fourth year (salary scale P). You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20. Additional Employment Conditions Work and science require good employment practices. This is reflected in Radboud University's primary and secondary employment conditions. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself, for example, exchange income for extra leave days and receive a reimbursement for your sports subscription. And of course, we offer a good pension plan. You are given plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes. We are You will be embedded in the Data Science section of the Institute for Computing and Information Sciences (iCIS) at Radboud University in Nijmegen. iCIS conducts world-class research in machine learning, software science and digital security, and consistently ranks among the top computer science institutes in the Netherlands. The atmosphere is informal, international and collaborative. The SHARE consortium (funded by the NWO Knowledge and Innovation Covenant programme) brings together Radboud University, the Dutch Open University, DSO Alliander, national metrology institute VSL, Zenmo, Bronscode and the Municipality of Nijmegen, spanning AI, privacy engineering, energy systems, law and governance. You will be supervised by Dr Yuliya Shapovalova (probabilistic machine learning, time series) and Prof. Tom Heskes (machine learning and artificial intelligence). Practical information and applying You can only apply via the button below. Address your letter of application to Yuliya Shapovalova. In the application form, you will find which documents you need to include with your application. We look forward to receiving your application. In your motivation letter, please address the following questions among other things: Which generative modeling approach you would use as a starting point for energy time series on a physical network, why you would choose it, and what you expect the biggest challenge to be. We are interested in your reasoning rather than a particular answer. A software application or data analysis project that you are proud of: what it does, what was challenging about it, and what your own contribution was. This may be a personal project, coursework, or work completed for a company. Including a link is optional. A one-paragraph description of your MSc thesis, written for a reader outside your field. You will preferably start your employment on 1 January 2027. We can imagine you're curious about our application procedure. It describes what you can expect during the application procedure and how we handle your personal data and internal and external candidates.

Core Responsibilities

You will develop physics-informed, domain-constrained generative models to create synthetic energy data for grid planning and decision-making. Additionally, you will collaborate with a research team to integrate privacy guarantees and contribute to an open-source synthetic data toolbox.

Requirements

You must hold an MSc degree in computer science, AI, data science, applied mathematics, physics, or electrical engineering. You should have a solid background in machine learning, strong Python programming skills, and experience with deep learning frameworks.

Benefits

• 8% Holiday allowance
• 8.3% End-of-year bonus
• Flexible working hours
• Work from home options
• Pension plan
• Training and development schemes
• Reimbursement for sports subscription
• Extra days off