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and toolsets for engineering measurements relevant to clinical settings. The project will be supervised by experts in DIC (Hari Arora), surgery (Iain Whitaker) and wider biomaterials imaging research
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to constrain the depth of the magmatic pressurization source (5). Training The candidate will gain skills in seismic data processing, tomographic imaging, and numerical modelling. Travel opportunities include
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data-driven approaches, multi-scale model development and software development depending on the interest of the successful applicant. Big picture: The Tarzia Research Group (https
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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several benefits, including thermal conductivity, electrical insulating and creating the necessary structural integrity needed around the battery. However, this process can be slow, induces an element of
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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-disciplinary PhD project aims to provide a clear picture of the landscape of battery manufacturing, waste and end-of-life processing. The project aims are to: Identify waste streams and energy requirements
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skills, including MS Office and other programs e.g. photoshop High-resolution confocal imaging experience Basic programming skills Excellent knowledge of Drosophila genetics Very good ability to explain
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ResTOrES project will develop, test, and demonstrate a prototype resilience assessment toolkit for offshore energy systems. The toolkit will enable the quantification of resilience in terms of appropriate
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- and time-specific innervation that extends into adolescence. Our lab has used whole-brain tissue clearing, light-sheet imaging, and machine learning to map the spatial and temporal dynamics of serotonin