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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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for biology and healthcare; then, the Chair of Biological Imaging (CBI) at the Technical University of Munich (TUM), and its integrated Institute of Biological and Medical Imaging (IBMI) at the Helmholtz
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to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them for downstream tasks. In this project, you will develop novel unsupervised machine learning methods
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focus on understanding how axons maintain their structure and function, and how these processes break down in disease. You will have the opportunity to contribute to one of our ongoing projects addressing
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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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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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Lars Åke Andersen 15th September 2025 Languages English English English The Faculty of Health Sciences PhD Candidate in Cardiac Biology at the Department of Medical Biology Apply for this job See
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at higher risk offered PSA blood tests which are not definitive. Our research aims to develop an image-based approach to screening, combining PSA testing with MRI to better identify aggressive cancers