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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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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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(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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learning patterns in unlabelled medical images and then leveraging them for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images
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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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assessments. Focus on the brain and psychology research outcomes, using these to produce original research outputs for publication in medical journals. Master cutting-edge laboratory assessments of health and
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for these infants. Structural brain imaging with cranial ultrasound (CUS) and magnetic resonance imaging (MRI) remain the traditional means of neurological assessment but are limited in their ability to reliably
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by thousands of genes and their interactions with environments and lifestyles. The research will take a new approach using data science and medical imaging to understand how biological age can be
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perfusion as people age with various comorbidities (hypertension, hypotension, obesity) will preserve oxygen supply to the brain. MRI and two photon imaging are used in animal models to understand
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-derived criteria. A scoping review, alongside initial semi-structured interviews and focus group transcripts subjected to thematic analysis, will build a picture of what key stakeholders want. Clinic