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annotations are scarce or unreliable. Recently developed unsupervised learning methods allow to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them
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, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where annotations are scarce or unreliable. Recently developed unsupervised learning methods allow
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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studies of aluminium precipitate evolution during forming; or development of solid‑state joining techniques for dissimilar lightweight metals. Prior research experience in materials processing
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sensor spoofing. These attacks manipulate input data while maintaining apparent operational normality, potentially leading to unsafe decisions without detection. This project aims to develop a novel
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Paediatric services, including KCH), and age- and gender-matched healthy controls. Parts 2 and 3 are under development. Part 2 will involve a targeted neuroimaging (EEG and/or fMRI) intervention study with a
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treatments. To date, there are few techniques that integrate AI and digital twins to improve patient outcomes. Your Role In this project, you will develop new methods that combine AI and digital twins
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treatments. To date, there are few techniques that integrate AI and digital twins to improve patient outcomes. Your Role In this project, you will develop new methods that combine AI and digital twins
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Project details Early detection of cognitive decline allows identification of those at high risk of developing dementia when medical treatments may be effective in preventing disease onset. Our
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details This is an outstanding development opportunity for a talented individual who wishes to conduct research on children and young people with life limiting conditions and their families. Previous