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Imaging of Materials Facility (AIM ) led by Professor Richard Johnston and Swansea University's Simulation and Immersive Learning Centre (SUSIM ). The student will develop novel medically bespoke protocols
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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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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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early ‘prodromal’ stages) is yet to be established in large community settings. This PhD project will examine the effectiveness of AI-based analysis of eye images in predicting cognitive/neurodegenerative
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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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PhD title: Mixed Methods Study of Depression Research Clinics as a means of addressing the primary-secondary care gap in NHS service provision. Principal supervisors: Dr Neil Nixon (School
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related PhD projects on behavioural change and weight loss. The successful doctoral candidate will lead a randomised controlled trial, including participant recruitment and coordination of laboratory
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outputs for publication in medical journals. Master cutting-edge laboratory assessments of health and imaging using 3 Tesla MRI scanning to investigate skeletal muscle and energy metabolism biomarkers
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, machine learning techniques may be integrated to accelerate simulations and improve medical image processing, ultimately aiding in stroke diagnosis and treatment planning. Please note that this is a self