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Research theme: "Next Generation Wireless Networks", "Signal Processing", "Machine Learning" UK only How to apply: uom.link/pgr-apply-2425 This PhD project aims to design novel resource allocation
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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class is a collection of models that includes the random growth of a surface over time or the behaviour of a large number of particles that move around in space and interact with each other according
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suite of analytical tools to understand the properties of engineering plastics and understand their stability and behaviour during recycling leading to improved quality of recyclate and enhanced
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for mechanical coupling. We are currently investigating the rich array of mechanochemical behaviours displayed by catenanes, rotaxanes, and knots. We have recently described a rotaxane-based molecular device
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to also improve and scale the process. We have made major contributions in this area, including the use of Machine learning to discover new cryoprotectants [Nature Communications 2024, 15, 8082
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characterisation in-situ. This PhD project will investigate fundamental factors that drive electrical tree growth using this methodology. The research will conduct comprehensive measurement of under precisely
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sluggish diffusion kinetics of HEAs make them excellent candidates for resisting oxidation and corrosion in high-temperature steam. Guided by thermodynamic modelling and machine learning, we will identify
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of reinforcement learning or agent-based systems. LanguagesENGLISHLevelExcellent Research FieldComputer science » Computer systemsYears of Research Experience1 - 4 Additional Information Benefits • Full funding