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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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techniques, including fluorescence microscopy, microfluidics, and biomolecular encapsulation. You will collaborate with leading scientists at the University of Surrey and international research partners (Prof
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Testing (NDT) – a crucial technique used in industries to assess materials, components, and structures without causing damage. This PhD project focuses on advancing Ultrasound Testing and X-ray Computed
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the effectiveness of an audio/visual online coaching programme that leverages digital health technologies as part of a novel intervention that can be easily accessed by a wide audience. This will be compared with
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personalized. The candidate will investigate textiles, computational design, and functional validation of hand-worn assistive devices, aiming to create textile exoskeletons that can conform to complex hand
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systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing modelling capabilities for the prediction of energy
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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins The ability to simulate initiation and detonation effects within energetic materials is a significant capability gap
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Are you passionate about cognitive computational neuroscience and eager to contribute to cutting-edge research in perception? We invite applications for a fully funded PhD studentship supported by