Development of machine and deep learning methods to predict marine extremes for engineering and navigation purposes

The PhD candidate is expected to develop an integrated framework that combines machine and deep learning methods with statistical and mathematical approaches to analyse and forecast marine extremes, including sea level, sea surface temperature, sea ice, and surface waves, from coastal to offshore regions. Understanding and predicting these extremes is increasingly important for engineering design, coastal management, and navigation under a changing climate.

The research is motivated by the unique availability of long-term and high-resolution marine datasets in the Baltic Sea region, including in situ observations, remote sensing products, and hydrodynamic model outputs. These datasets can be unified through a geoid-based vertical reference system, enabling consistent and accurate analysis of marine variables across the coastaloffshore areas.