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Breadcrumb Home Working at Job opportunities PhD Position: Physics-Informed Generative AI for Synthetic Energy Data PhD Position: Physics-Informed Generative AI for Synthetic Energy Data Employment 1.0 FTE Gross monthly salary € 3,204 - € 4,051 Required background Research University Degree Organizational unit Faculty of Science Application deadline 25 October 2026 Apply now 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 (link Is External) (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. At Radboud University, I can fully focus on expanding my expertise while learning from my peers and mentors. Noemí Segura-Solé PhD candidate in Microbial Ecology Noemí Segura-Solé is a PhD candidate in Microbial Ecology. “After completing my Master’s degree in Norway, I looked at several European universities for a PhD position, including the university where I studied. Then I saw a job opportunity at Radboud University, which aligned perfectly with my interests. After a great first impression of my supervisors during the job interview and positive feedback about Radboud University from my network, I decided to take the leap and move to Nijmegen. It was a risk to leave my Norwegian comfort zone, but I am extremely satisfied with my choice. From the start, I immediately felt welcomed by the whole department. I have many social interactions with other PhD candidates, and the senior researchers are very approachable and interested in my research. The working conditions for PhD candidates here are also excellent: a good salary, plenty of days off, and flexible working hours. The latter is an added bonus because I already had two children when I started my PhD. Thanks to these factors, I can fully focus on expanding my expertise while learning from my peers and mentors.” Does this sound like you? 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. What we offer you 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. Radboud University's primary and secondary employment conditions reflect this. 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 membership. In addition, you receive a 34% discount on the sports and cultural activities at Radboud University as an employee. And, of course, we offer a good pension plan. We also give you plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes. Where you will be working 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). Faculty of Science The Faculty of Science (FNWI), part of Radboud University, engages in groundbreaking research and excellent education. In doing so, we push the boundaries of scientific knowledge and pass that knowledge on to the next generation. We seek solutions to major societal challenges, such as cybercrime and climate change and work on major scientific challenges, such as those in the quantum world. At the same time, we prepare our students for careers both within and outside the scientific field. Currently, more than 1,300 colleagues contribute to research and education, some as researchers and lecturers, others as technical and administrative support officers. The faculty has a strong international character with staff from more than 70 countries. Together, we work in an informal, accessible and welcoming environment, with attention and space for personal and professional development for all. Radboud University At Radboud University, we aim to make an impact through our work. We achieve this by conducting groundbreaking research, providing high-quality education, offering excellent support, and fostering collaborations within and outside the university. In doing so, we contribute indispensably to a healthy, free world with equal opportunities for all. To accomplish this, we need even more colleagues who, based on their expertise, are willing to search for answers. We advocate for an inclusive community and welcome employees with diverse backgrounds, cultures, and perspectives. If you want to learn more about working at Radboud University, follow our Instagram account (link is external) and read stories from our colleagues. Is this the job for you? 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. Apply now Application deadline 25 October 2026 We would like to recruit our new colleague ourselves. Acquisition in response to this vacancy will not be appreciated. Want to know more about this position? Dr Y. Shapovalova (Yuliya) [email protected] Last modified: 18 September 2026 Share this page

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 build an open-source toolbox and publish findings at leading machine learning venues.

Requirements

You must hold an MSc degree in computer science, artificial intelligence, data science, applied mathematics, physics, or electrical engineering. Strong programming skills in Python and experience with deep learning frameworks like PyTorch are required.

Benefits

• Holiday allowance
• End-of-year bonus
• Flexible working hours
• Work from home
• Pension plan
• Training and development schemes
• Sports and cultural activities discount