Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines

The position addresses critical challenges faced by modern wind turbines operating under changing climate conditions, shifting wind patterns, and structural ageing. As turbines experience evolving loads, discrepancies arise between physical assets and their nominal digital designs, complicating accurate prediction of structural behavior and sustainable lifecycle management. This research aims to overcome these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and efficient digital twin frameworks for real-world wind turbines.

Position information: