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Field
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focus on the medical image processing aspect of the Birth4Cast simulator by researching and developing automated image segmentation procedures to extract the pelvic floor muscle complex and the fetal head
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plastic use by nesting birds globally using image archives such as YouTube, Google Images etc. and relate it to land-use (e.g. urbanisation), pollution etc. For further information on this project and
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editing), transcriptomics, laser scanning confocal microscopy, computational imaging approaches for analysis of images and movies, stimulating neuronal function with opto- and thermo-genetics in vivo, and
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culture and in vitro models of mucosal immunity Data analysis using Python and digital image processing These skills are highly transferable to careers in biomedical research, pharmaceutical development
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, FTIR, terahertz time domain characterisation and thermal imaging). The student will also gain transferable skills in project management, publishing and public engagement. The project benefits from a
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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have the opportunity to develop their computational modelling capabilities in this project, alongside learning new skills such as testing on biological tissues and medical image processing
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scanning confocal microscopy and calcium imaging in time-lapse, computational imaging approaches for analysis of images and movie recordings, analysis of the connectome to identify neural circuits
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results from human decision-making to inform the design of this new paradigm, and feed the results of the latter back into human decision-making to help make it more explainable. The PhD student will: (1
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well as reliability in extracting target features. The project covers UK tuition fees and the standard UKRI PhD stipend and it is co-funded by the Quantum Hub in Sensing, Imaging and Timing (QuSIT) and BAE Systems