Doctoral Student in Probabilistic Machine Learning for Fluid Mechanics

KTH Royal Institute of Technology seeks a highly motivated doctoral student to join the Fluids and Surfaces Group within the FLOW community. The position focuses on developing probabilistic machine learning methods for fluid mechanics, particularly for turbulent flows carrying electric charge, such as those in power transformers. The project aims to integrate simulations and experimental data into reliable tools for design, prediction, and control of complex fluid systems, with a focus on quantifying uncertainties and optimizing experiments. The work is supervised by Shervin Bagheri and Ozan Öktem and is funded by Digital Futures and Hitachi Energy. The successful candidate will pursue a doctoral degree while contributing to safer and more sustainable power infrastructure.