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Field
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) milling. This programme will involve state-of-the-art imaging approaches to visualise molecular details of the intricate relationships between chromatin and nuclear condensates. The successful candidate
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learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image analysis, that is medical images that evolve over
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to the POSSEM trial, using photon-counting coronary CT angiography, advanced imaging, and computational modelling to identify patients at risk of accelerated coronary artery disease and enable more personalised
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measurements: transepithelial resistance, muscle contraction Inflammation level measurements: RT-qPCR, ELISA, bioplex Immunocytochemistry, imaging and 3D reconstruction Flow cyrtometry Single-cell transcriptomic
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implementation of elastomer adhesives Optimization of formulation for 3D printing and 2D printing Contribute to ongoing ionic liquid formation projects Design and prototype and scalable processing Collaborate with
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staining and confocal imaging, RNA-seq. Data analysis, interpretation of experimental outcomes, and preparation of manuscripts for publication Supervise and train junior researchers and contribute
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pathomechanisms and developing prototype drug candidates. Project outcomes will be protected as intellectual property and will provide a foundation for translation through clinical studies and appropriate
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, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system
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reconstruction, processing, synthesis, and registration, as well as AI for treatment outcome prediction and clinical decision making. The projects will involve using multi-modality images (CT, CBCT, MRI, PET
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Program and will work closely with the staff of the Image Collection and Fieldwork Archives (ICFA) to process the Justin Kerr Collection, which consists of photographs of approximately 6,000 unique