The offshore wind industry is expanding rapidly, with 120 GW expected to be installed in Europe by 2035. In this fast-growing and competitive context, the long-term reliability of offshore wind farms critically depends on their ability to withstand evolving marine conditions over operational lifetimes exceeding two decades. Climate change is already modifying wind regimes, wave climates, and the frequency and intensity of extreme events (Amlashi, 2024), introducing significant uncertainties into the design criteria of offshore structures (Barkanov et al., 2024). These uncertainties are particularly important in energetic regions that also offer the greatest potential for offshore wind development (Susini et al., 2022).
Metocean analyses therefore play a central role in characterizing the environmental loads that offshore assets will face throughout their life cycle. Extreme values of design parameters such as significant wave height, wind speed, and total water level directly influence structural safety, fatigue life, and operability (Slater et al., 2021). Significant wave height, for example, governs wave loading and is a key design and certification parameter (Zhang et al., 2019).
Recent studies highlight the need to revisit traditional, stationary extreme value approaches by integrating climate-driven trends using nonstationary statistical models or multimodel climate ensembles (Morim et al., 2019; Lobeto et al., 2023). Developing such methods is essential for improving the robustness of offshore wind design in a changing climate. This postdoctoral project will contribute to that effort by advancing the analysis and characterization of future metocean extremes relevant to offshore wind engineering.
The successful candidate will analyse main metocean design variables (wind speed, wave height and period, etc…) from climate models and assess the impacts of climate change on the extreme part of these variables and associated uncertainties. The following tasks will be covered:
The successful candidate will be supervised by engineers and researchers from Ifremer, IPSL, BRGM, and France Energies Marines.
PhD in mathematics/statistics, machine learning, climate sciences, meteorology, oceanography
A first experience in statistical modeling of extremes is required
In accordance with regulations, all other things being equal, priority will be given to people with disabilities.
