项目介绍

2026年4月22日

The energy transition is one of the most urgent challenges of our time. The electricity grid is under increasing pressure due to electrification, renewable energy growth and rising demand, and AI is key to solving it. As a Postdoctoral Researcher in AI-based Load Forecasting at Radboud University, you will be at the heart of this challenge. You will develop cutting-edge AI models that predict electricity consumption at both grid and individual building level, directly contributing to reducing network congestion in the Netherlands. Your work will have real-world impact: the models you develop will be tested and validated together with industry partners such as Alliander and Stedin and made openly available to the broader AI and energy community.

You will conduct research on advanced AI methods for short-term load forecasting as part of the FlexLab AI Innovation Lab, a collaboration between Radboud University, Alliander, Stedin, the Netherlands Organisation for Applied Scientific Research (TNO), Eindhoven University of Technology (TU/e), HAN University of Applied Sciences and several SME partners. FlexLab develops, tests and validates AI technologies for flexible energy management with the goal of tackling network congestion in medium- and low-voltage grids. Radboud University leads the load forecasting work package (WP4), and you will be the key researcher driving this work forward.

Your research will focus on developing and validating AI models for load forecasting in scenarios where current models fall short, such as extreme weather events, grid incidents and high variability in renewable energy. You will explore techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and integrate your models into the open-source OpenSTEF platform and the Linux Foundation Energy ecosystem.

Does this sound like you?

• You hold a PhD in Computer Science, Artificial Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field.
• You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related applications.
• You have experience with or a strong interest in graph neural networks, Bayesian methods or uncertainty quantification techniques.
• You are motivated to conduct applied research in close collaboration with industry partners and SMEs.
• You have a track record of scientific publications in relevant venues.
• You are proficient in Python and relevant machine learning frameworks such as PyTorch or TensorFlow.
• You have excellent communication skills in English, both written and spoken.
• You are a proactive team player who can work effectively in a multidisciplinary and multi-institutional consortium.
• You are committed to open science and are enthusiastic about making your research results openly available.

What we offer you

• We will give you a temporary employment contract of 28 months.
• Your salary within salary scale 10 depends on your previous education and number of years of (relevant) work experience. The amounts in the scale are based on a 38-hour working week.
• 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 department at Radboud University, Nijmegen. The department is an active research group with a strong focus on machine learning, AI and data-driven applications. Within the FlexLab consortium, Radboud University plays a leading role in AI-based load forecasting, collaborating closely with network operators Alliander and Stedin, research institute TNO and a range of innovative SMEs. You will be part of a dynamic, interdisciplinary team working on one of the most societally relevant AI challenges in the Netherlands today.

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 and read stories from our colleagues.

Is this the job for you?

You can apply only 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.

The first interviews will take place on Tuesday 2 June. You will preferably start your employment on 1 September 2026.

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.

Do you already work at Radboud University and have questions about making an internal move? Learn more about internal applications.

You will work closely with SME partners in short-cycle innovation trajectories of 6 to 18 months, translating scientific advances into practical prototypes to be tested in real or simulated environments. You will contribute to scientific publications, open-source releases and knowledge-sharing events with the broader energy and AI community.