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as the Insigneo Institute theme co-director for Healthcare Data/AI and the N8 Centre of Excellence in Computationally Intensive Research theme lead for Machine Learning. About the School/Research Group
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foundations, combining ultrasonic guided wave monitoring, high-fidelity finite element simulations, Bayesian inference, and machine learning. Guided waves can propagate over long distances and reach areas
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speech models to predict communication decline and intelligibility changes for longitudinal monitoring; Design machine learning methods to model relationships between vocal characteristics and impact on
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developing a computational model that simulates blood flow for ICH patients. The research will exploit a powerful new approach — physics- informed neural networks (PINNs) — that combines machine learning with
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Neuro-Symbolic AI for Trustworthy Clinical Decision-Making: Bridging Linguistic Fluency and Logical Reasoning in Large Language Models (S3.5-COM- Valentino) School of Computer Science PhD Research
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physical systems. You will explore how the dynamic behaviour of nanomagnetic devices can be used to realise these KAN functions directly in hardware. Working with a combination of modelling, machine learning
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survival is shaped by resource availability, competition, and cultural knowledge of food resources. You will work with an established bioacoustic dataset, a validated call machine-learning classifier, and a
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BBSRC Yorkshire Bioscience DLA Programme: Directing Stem Cell Fate – The Role of Ribosomal RNA Modifications School of Biosciences PhD Research Project Competition Funded Students Worldwide Dr E
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device health status through condition monitoring. AI techniques such as machine learning will be used to optimise gate driver performance and to map gate drive signal attributes to power device health
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Neuro-Symbolic AI for Trustworthy Clinical-Decision Making: Bridging Linguistic Fluency and Logical Reasoning in Large Language Models (S3.5-COM- Valentino) School of Computer Science PhD Research