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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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with children and an interest in educational practices, speech and language development, and outdoor learning is highly desirable. Familiarity with programming and data analysis tools is also preferable
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, and outdoor learning is highly desirable. Familiarity with programming and data analysis tools is also preferable as the PhD will involve some advanced quantitative data analysis, although support will
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with children and an interest in educational practices, speech and language development, and outdoor learning is highly desirable. Familiarity with programming and data analysis tools is also preferable
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(Dr Jun Jiang) (2) In-situ formability, microstructure analysis and forming process optimization (Prof Li-Liang Wang) (3) Crystal plasticity modelling to understand how microstructural features caused
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the opportunity to co-author working papers with the PI team. The majority of tasks will focus on digital source collection and analysis, working closely with the P.I. team, with some additional support
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exploring the economic or value contexts in which politics operates. Similarly, sound economic analysis requires an awareness of how resource allocation is conditioned both by the values and principles
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the royal household with a digital humanities analysis of existing databases of Londoners and immigrant communities. This project will be jointly supervised by Dr Justin Colson and Dr Adam Chapman, Institute
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that restricts its applicability. While chromatography can provide this pre-enrichment, High-Performance Liquid Chromatography (HPLC) can be costly and complex. A cost-benefit analysis reveals diminishing returns