The Ph.D. project aims to conduct research and innovation in flow simulation in 
offshore wind turbines by combining high-fidelity computational fluid dynamics 
(CFD) models with machine learning (ML) models. The developed state-of-the-art 
models are expected to be integrated into future digital twin technology to 
fulfill the industrial needs of computationally efficient tools and form a basis 
for condition monitoring, prediction, analysis, and maintenance of offshore wind 
turbines. Key research areas that will be exploited in the Ph.D. research 
project will be physics-based and data-driven Reduced Order Models (ROM) to 
simulate and predict wind farm flows, especially in the wake region.

Required Academic qualifications

MSc in Engineering, Applied/Numerical Mathematics, Atmospheric Sciences, 
Computer Science, Data Science. Physics or similar.
Skills in numerical modeling and computational fluid dynamics (CFD).
Experience in C, C++, Python or similar.
Experience in use of opensource CFD softwares (OpenFOAM etc.).
The following experiences and skills will be emphasized:

Self-driven with a strong ability to work independently when required.
Experience in machine learning and deep learning.
Coding Skills in Python using well-known libraries, e.g., TensorFlow, Sklearn or 
similar.
Conduct independent research and publish the result in recognized scientific 
journals.