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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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access to an NWSSDTP Research Training Support Grant for eligible research expenses. Application process Applications for this ESRC CASE PhD Studentship should be sent by email to the School of Law and
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various chemical additives, dyestuff, pharmaceuticals as well as the precursor of photo initiator. However, in the conventional process, the synthesis of above-mentioned C-C coupling products focuses
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at the interface between stochastic modelling, signal processing and data science. Ultimately, the project will develop key indices that can be used to assess the health of the soil ecosystem. Such indices
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(AI)-driven speech and voice analysis present transformative opportunities to enhance emergency call triaging. For instance, identifying specific audio features, such as laboured breathing or vocal
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and Language Sciences, including our Speech and Language Sciences subject area. The postholder should have a PhD in a relevant discipline e.g. Speech and Language Sciences, Education, Psychology
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team and the School of Education, Communication and Language Sciences, including our Speech and Language Sciences subject area. The postholder should have a PhD in a relevant discipline e.g. Speech and
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, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination at the point of commencing this post
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the interpretability of these models can be enhanced to support clinical decision-making. This project will leverage the complementary expertise of both supervisory teams in EEG signal processing, graph deep learning
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well they meet the following criteria: A first class (or high upper second) honours degree in Engineering, Physics or equivalent Excellent English written and spoken communication skills An interest in