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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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trustworthy operation of navigation systems in complex, GNSS-denied scenarios. The ultimate goal is to provide the navigation research community and industry with tools and methods that ensure continuous, high
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with UKAEA, providing opportunities for engagement with leading fusion research facilities. Together, they offer a wealth of experience in integrating simulation and experimental methods to solve real
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research opportunity focuses on advancing large-scale additive manufacturing using metal wire as feedstock and electric arc as the heat source. The project aims to develop an innovative and efficient method
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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validated surface functionalisation methods that significantly improve metascintillator performance, accelerating the development of advanced radiation detectors for ToF-PET and enhancing early cancer
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-based method to approximate the CFD-revealed effects of liquid metal convection on molten pool temperature predictions. • Designing and conducting instrumented WA-DED experiments to validate the developed
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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. However, inefficiencies in wind turbine control and maintenance lead to increased operational costs and reduced energy output. Traditional maintenance methods rely on reactive or time-based servicing, which