521 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" "Univ" "UNIV" PhD scholarships in United Kingdom
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computational design, Industry 4.0 integration, digital twins, and data-driven optimization to enhance manufacturing efficiency. Working closely with the NWCAM2 companies, this project aims to reduce waste, embed
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are emerging as promising alternatives, offering potential benefits in carbon reduction, durability, and ageing resistance. However, widespread adoption is limited by inconsistent performance data, insufficient
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should hold a Master’s degree in Economics or a related field (e.g., Finance, Psychology, Maths or Data Science), with a foundational understanding of behavioural/experimental methods and/or econometrics
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geophysical and planetary models. IBM faces several such challenges in practice, many of which are currently approached using Foundation-Model (FM) surrogates. However, certain inverse problems arise in data
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and in-depth information about their vision. This could lead to a paradigm shift for monitoring vision in patients with MS – enabling earlier and more reliable detection of vision-related manifestations
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop