PhD Studentship: Development of Innovative and Efficient Computational Fluid Dynamics Simulator based on Physics-Informed Neural Networks
Develop a physics-informed neural network-based computational fluid dynamics simulator for offshore renewable energy applications, integrating deep learning with fluid dynamics models and embedding the solution into OpenFOAM. The role combines PhD research with teaching, focusing on improving simulation efficiency and robustness for complex engineering problems.