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electrochemical processes (h-index 23, i10-index 43). This studentship is supported through collaboration with leading partners in precision manufacturing sectors such as the company LoadPoint Ltd. Successful
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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Research Platform (SARRP) and in vivo imaging to develop image-guided radiotherapy (IGRT) strategies for liver and childhood brain cancers. A key focus of this project is hepatocellular carcinoma (HCC
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investigate the feasibility of new imaging modalities for situations where currently employed imaging techniques, such as X-ray transmission and backscatter, have limitations. This project will focus
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(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT
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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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involve leveraging advanced natural language processing and medical image analysis to transform imaging data into clinically relevant information. Additionally, it will explore the use of multimodal fusion
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settings. The project will be supervised by experts in DIC (Hari Arora), surgery (Iain Whitaker) and wider biomaterials imaging research at Swansea University (Richard Johnston), building on decades
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function and its role in healthy development and disease. There is no brain-imaging method that allows us to measure infant brain activity with high resolution in both spatial and temporal dimensions