MetaWing: Intelligent and Adaptive Reconfiguration Framework for Enhanced Aerodynamic Efficiency under Gust and Turbulence

We are seeking motivated PhD candidates to join a project focused on applying machine learning to address fluid dynamics challenges in bio-inspired flight/swimming and renewable energy systems. You will work closely with Dr. Juan Li and a second supervisor at King’s College London’s Strand Campus. Dr. Li is an expert in unsteady aerodynamics and renewable energy. The PhD offers flexibility, allowing candidates to shape their research as long as it aligns with the following themes:

1. Bio-Inspired Flow Systems:

·      Develop numerical and machine learning techniques to model natural flight and swimming, or cardiovascular flow problems.

·      Investigate the underlying mechanisms and design advanced control and optimization strategies for bio-mimetic systems.

·      Work on data-driven surrogate models to replace traditional CFD simulations using AI techniques (GNNs, RNNs, LSTM) for 2D/3D bio-inspired systems, cardiovascular flow, fluid-structure interaction, or control and optimization.

2. Renewable Energy Systems:

·      Focus on intelligent modeling, control, and optimization of flow in wind turbines, farms, and PEM fuel cells.

·      Perform numerical simulations for fluid-structure interaction in wind turbines or multi-disciplinary, multi-scale problems in PEM fuel cells.

·      Develop data-driven modeling frameworks and provide optimization solutions for next-generation PEMFCs to improve efficiency, reliability, and longevity.

Preferred Candidate Qualifications:

·      Academic Background: Bachelor’s degree in Computer Science, Engineering, Physics, Applied Mathematics, or a related field, with a strong academic record (minimum GPA of 3.5/4.0 or equivalent).

·      Programming Skills: Proficiency in Python, C++, MATLAB, and experience with version control (Git).

·      Research Experience: Demonstrated experience in deep learning, fluid mechanics, or related fields through research or internships. Familiarity with machine learning frameworks like TensorFlow or PyTorch is preferred.

·      Publications: Prior publications in peer-reviewed journals or conferences are a plus.

·      PINNs: Experience with Physics-Informed Neural Networks (PINNs) is highly desirable.

Supervision: The PhD will be supervised by Dr. Juan Li (Juan.Li@kcl.ac.uk ). For more information, please get in touch.

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Application Details:

To be considered for the position candidates must apply via King’s Apply online application system. Details are available at: https://www.kcl.ac.uk/engineering/postgraduate/research-degrees

Please apply for Engineering Research (MPhil/PhD) and indicate your desired supervisor, funding ref, and the project title in your application and all correspondence.

Supervisor name: Dr. Juan Li

Funding reference: EngNMESLi

Project title: MetaWing: Intelligent and Adaptive Reconfiguration Framework for Enhanced Aerodynamic Efficiency under Gust and Turbulence

The selection process will involve a pre-selection on documents, if selected this will be followed by an invitation to an interview. If successful at the interview, an offer will be provided in due time.

https://www.kcl.ac.uk/study/postgraduate-research/how-to-apply