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Field
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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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in Biomedical Engineering Mode of study: Full-time Location: Bay Campus (predominantly) Project description: This project aims to enhance best practices in strain quantification for biomedical
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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learning models (i.e. keeping an eye on the system’s "brain" to quickly spot when it starts to struggle or behave unexpectedly). Our goal is to create tools that continuously evaluate the performance
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and exact optimization methods enhanced by machine learning (ML). The overarching goal is to solve large-scale combinatorial optimization problems more efficiently, particularly in domains such as
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Annual tax-free stipend of £22,780/year for 4 years, full coverage of tuition fees for UK/Home Students, plus training/travel funds Placed On: We invite applications for a fully funded PhD research
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increase healthcare access. Further, such technologies can facilitate remote training monitoring and performance enhancement for athletes. Developing accurate, low-cost, comfortable technologies
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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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. The successful candidate will be based in the Mechanical and Aerospace Systems research group (previously known as G2TRC) within the faculty of Engineering and will be part of a supportive team of 50 researchers
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development (using both traditional signal processing and machine learning), antenna design, and system hardware development. We collaborate closely with clinical experts to develop innovative technologies