Required qualifications:
1. To have a Master degree at the start of the project (1 Oct 2025) in earth - or environmental science, climatology or related field (geography, engineering, bioscience engineering, geology, physics, meteorology, oceanography, mathematics, informatics, etc.);
2. To have experience in computational programming;
3. To have demonstrated verbal and written communication skills in English;
4. To have a collaborative mindset: Enjoys working with others to achieve common objectives.
To reach net-zero greenhouse gas emissions by 2050, offshore wind capacity in the North Sea is expected to expand dramatically. However, current planning and assessment methods often neglect or simplify dynamic climate and economic conditions, despite the typical 30-year lifetime of offshore wind farms. As installations grow in size and density, intra- and inter-farm wake effects become more pronounced, significantly altering wind availability and energy yield. At the same time, climate change introduces uncertainties in wind patterns, while offshore wind energy projects are moving away from subsidies and becoming more exposed to volatile electricity markets. These developments call for a more integrated approach to offshore wind assessment that accounts for interactions between climate, turbine layout, market forces, and turbine degradation over time.
This PhD project aims to develop and validate a layout-aware wind farm parameterization for use in kilometre-scale climate model, and use this to assess long-term energy production under changing climate and increased wind farm capacity in the North Sea. The research is organized into three phases: Phase 1 centers on the development and validation of a new parameterization. A machine learning-based approach will be employed to replace a recently developed physical method for representing wind farms in a mesoscale model. This parameterisation will then be integrated into a regional climate model to more accurately capture turbine interactions compared to current techniques. Phase 2 involves applying the improved regional climate model to simulate historical and near-future climate conditions. These simulations account for both global climate change and local wind field alterations caused by the development of neighboring wind farms. Phase 3 explores how various design configurations—such as turbine placement, turbine type, and capacity density—impact power production under future wind climate scenarios. These configurations will be selected in collaboration with stakeholders to ensure practical relevance.
A consortium, comprising the Department of Earth and Environmental Sciences and the Department of Mechanical Engineering at KU Leuven, and the Department of Engineering Technology at VUB, is seeking a PhD student to join the project: "Addressing impacts of Changing Climates and Energy Markets on Offshore wind farms (ACCEMO)". You will collaborate with three other PhD students, two postdoctoral researchers, and a computer scientist. Your specific focus will be on regional climate modeling to assess future wind energy production in the North Sea under changing climate conditions. You will be supervised by Prof. Nicole van Lipzig, and your daily work will take place within the Regional Climate Studies Group of the Division of Geography and Tourism, part of the Department of Earth and Environmental Sciences. This research group specializes in regional climate modeling for offshore wind energy, the development of climate scenarios, and the study of extreme weather events such as heavy precipitation and heat stress. Their projects span Europe, East Africa, and Antarctica. The team has strong expertise in atmospheric processes, land-sea-atmosphere interactions, and the impacts of climate change.
5. PhD Position;
6. Starting date preferably 1 October 2025, later date is possible;
7. Full time position for 48 months;
8. Location: Department Earth and Environmental Sciences, KU Leuven, Leuven, Belgium ;
9. Net monthly salary of about 2400 €, depending on personal situation. This includes social insurance. Transportation to and from the workplace is covered;
10. Contribution to educational activities is expected for about 10% of your time.