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will focus on the development of voice analysis technologies to enhance the prediction and triaging of Category 1 ambulance calls. Ambulance call centres play a critical role in triaging life-threatening
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evaluation, policy advocacy, or better understanding the contexts and causes of such abuse. The student will use advanced data science and applied statistics to enable combined analysis of different modes
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improves the performance of ROMs, making them more applicable to real-time structural health monitoring, vibration analysis, and control design. This research offers real-world impact across several
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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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You will lead the qualitative work to understand how individuals experienced the intervention and app how it worked. This will directly impact the design and refinement of the intervention, and so
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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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rheology, functional ingredients. Experience of data handling and statistical analysis. Track record of scientific writing (for career stage), with publications in peer-reviewed scientific journals
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the MET office. The student could choose to develop these connections to understand resident experience and behaviours, identify the current barriers and facilitators, to inform adaption measures
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, this interdisciplinary project will focus on developing robust, practical tools to assess and predict recyclate quality. The work will involve thermal analysis (e.g. DSC, TGA), rheology, mechanical testing, and molecular
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. Initial analysis suggests recurrent selection of divergent types in multiple locations. The aim of this role is to complete this analysis and prepare a manuscript for submission for publication