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methods to study human bone marrow models using high content imaging approaches. You will lead in designing and establishing new protocols to the laboratory as well as supporting, mentoring and training
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Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based on autofluorescence (AF) imaging
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in sustainability, photonic and quantum technologies, new nanostructured materials, sensing, imaging and clean energy. The group adopts an interdisciplinary approach to provide leading-edge research in
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in and knowledge of the broad area of soft matter, with expertise in microscopy, optical imaging, and data analysis. Experience in building and working with optical traps, and familiarity with colloid
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on autofluorescence (AF) imaging and Raman spectroscopy for detection of metastatic lymph nodes during breast cancer surgery. Engaging with and reporting to Dr Alexey A. Koloydenko (Department of
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soft matter, with expertise in microscopy, optical imaging, and data analysis. Experience in building and working with optical traps, and familiarity with colloid chemistry and surface functionalisation
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or orthotopic tumour models Supporting preclinical treatment studies involving standard-of-care or experimental agents Applying in vivo imaging techniques (e.g., bioluminescence imaging) to monitor tumour
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Waite and team on the ‘Evaluating the Usability and Acceptability of a Prototype AI-Powered Recovery Pathway Progress Dashboard (AI-ERPP Dashboard) for Tracking Postoperative Recovery by Registered Nurses
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team on the ‘Evaluating the Usability and Acceptability of a Prototype AI-Powered Recovery Pathway Progress Dashboard (AI-ERPP Dashboard) for Tracking Postoperative Recovery by Registered Nurses in
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fluorescence-lifetime detection (Fast-FLIM) and temporal focusing. This instrument will deliver quantitative, sub-second imaging of live three-dimensional cell-culture and organoid models, advancing fundamental