Postdoc Position: AI-based Load Forecasting for Energy Systems

1. Introduction

Contribute to one of the greatest challenges of our time. As a Postdoctoral Researcher, you will develop models that make the energy grid smarter and directly help reduce grid congestion in the Netherlands.

2. Job description

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.

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.

3. Profile

• 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.

4. We are

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.

5. We offer

• 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. 

6. Practical information and applying

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.

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