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of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British
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of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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Project details Objective: This project aims to develop a wireless, nanoengineered graphene-based biosensor for real-time dopamine (DA) detection. The wireless design of the sensor aims to enable chronic monitoring and facilitate seamless integration into dynamic neuromodulation systems for...
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This project is a fully funded MSc by Research Scholarship in Medicinal Chemistry / Synthetic Organic Chemistry and builds on a successful decade-long research programme at the University of Exeter
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behaviour in a practical, real-time monitoring system requires advances in both sensor engineering and behavioural data interpretation. This PhD project aims to develop a next generation environmental
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British
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at scale raises privacy and real hardware constraint concerns. This PhD will focus on those challenges by developing a distributed, privacy-preserving NILM framework, so we can move from small research
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costs [1]-[5]. Building on these previous findings, this PhD project will design a new MARL architecture that incorporates model uncertainty, cyber-attack scenarios, and network reconfiguration events