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is expected to soon be able to diagnose diseases occurring outside the retina. OCT images can be aberrated by the eye itself and imperfect optical design. Ocular imaging with adaptive optics promises
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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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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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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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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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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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-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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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