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arrhythmias, and 900,000 with heart failure. Medical imaging techniques, such as CT, MRI, and X-ray, are vital for diagnosing and guiding the treatment of cardiovascular diseases. This project aims to develop a
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frameworks. This approach is timely, as improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques
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improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques using accessible programming languages and
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Supervisors: Prof Ioan Notingher (School of Physics and Astronomy) Dr George Gordon and Dr Abdelkhalick Mohammad (Faculty of Engineering) Funding: fully-funded (stipend and PhD fees) Start date
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functioning. This research is only possible now thanks to 1) advances in imaging techniques that fuel a more detailed understanding of the brain, 2) tools from artificial intelligence that enable building
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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methodology to generate confidence in such decision, potentially reducing maintenance costs and down-time for offshore wind energy production. The images taken by each drone are loaded into the pre-processing
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power consumption. Many emerging biomedical devices, such as non-invasive and indwelling systems, require reliable operation and continuous feedback on biochemical conditions at the device–tissue
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Applications accepted up till Monday 15th December, 2025. Competition funded PhD Project. Supervisors: Dr Yi Feng (The University of Edinburgh), Dr Mattias Malaguti (The University of Edinburgh
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Deadline: Monday 15th December, 2025. Competition funded PhD Project. Supervisors: Dr. Chih-Jen Lin (The University of Edinburgh), Dr. Pierre O. Bagnaninchi (The University of Edinburgh) About