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scanning and measurement, garment fit evaluation, cloth simulation, digital twins, wearable sensing, smart textiles, and AI methods for clothing and body data analysis, to name a few. Applicants from
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human hearts and developing new methods to extract clinically meaningful information from them. Potential approaches include advanced signal decomposition and feature extraction, time–frequency and
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diagnosis methods that combine analytical approaches based on drivetrain physical properties with AI-driven data analysis techniques to enhance the accuracy and effectiveness of fault detection and
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, these methods can be subjective, time-consuming and difficult to scale. Meanwhile, mental health states may also be reflected in how people express, perceive and respond to the world through facial behaviour
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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, privacy-enhancing technologies, and trustworthy AI: stating requirements precisely, designing methods that meet them, and building prototypes fast enough to use. Applicants should hold, or expect to obtain
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innovative and dynamic sector, estimated at more than £100 billion and growing at over 6% every year. This project aims to deliver a step-change in hair care technology by developing new strategies to form
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readily damaged during routine styling treatments involving heat, chemical processing, and mechanical stress. The global hair care market is a highly innovative and dynamic sector, estimated at more than
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to build and characterise these DNA devices. The project will also explore optimisation methods to improve reliability, scalability, and performance in complex nucleic-acid-based systems. The student will
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Approximation calculations, whose direct use in Bayesian parameter estimation is currently computationally prohibitive. By providing a fast and statistically controlled surrogate for these calculations