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Field
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Modern cyber-physical systems (CPS), such as UAVs, next-generation fighter aircraft, and command-and-control (C2) platforms, integrate digital computation with physical processes to make mission
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skills. Main duties will include: conduct tissue-mechanical and imaging experiments using early avian embryos; acquire and process data; prepare reagents and samples; optimise protocols; program and debug
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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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processes that underly normal and abnormal cardiovascular and metabolic function and drive the translation of this strong basic science into advances in clinical practice. Our community of world-renowned
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awareness capabilities. The PhD will involve both simulation and experimental work. This includes designing and testing optical instrumentation and conducting observation campaigns to image and track
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-periodic structures, we can precisely control the interaction of radiation with matter, potentially achieving unprecedented timing resolution (sub-70ps) and significantly enhancing signal detection. This PhD
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different physico-chemical environments to drive self-organisation processes, like condensates, that shape mesoscale structures enabling tissue function. As a Postdoctoral Researcher at the Rosalind Franklin
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the Neurogenesis and Brain Cancer group led by Prof. Simona Parrinello. The team uses a variety of state-of-the-art technologies, including advanced imaging, single cell and spatial ‘omics’, barcoding, mass
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technologies such as single-cell transcriptomics, cerebral organoids, and advanced imaging to build our understanding of the biological processes behind the earliest stages of neurodegeneration and ageing
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Medicine (SCMMS) and Biomedical Engineering and Imaging Sciences (BMEIS) at King’s College London (St Thomas’s Campus). The post will suit someone with a PhD in the cellular mechanisms of injury in small