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optimisation algorithms to dynamically reconfigure the substation/distribution network settings to enhance the system efficiency. The optimisation algorithms will incorporate the uncertainties associated with
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the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real
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*Home fee status applicants from a Law background are invited to apply* The Centre for Doctoral Training in Safe AI Systems (SAINTS CDT) is the UK’s first multidisciplinary PhD programme focused
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and social acceptance. This research will develop an efficient variable renewable energy (wind and solar) input system architecture to produce, store, and distribute variable power output (electrical
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sweat distribution across impairment groups which may inform future clothing design for improved thermoregulation. Additionally, it will explore cooling interventions, using computational modelling
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. Water mass exchanges at this confluence, which are likely driven by submesoscale currents, play a key role in the distribution of heat, salt, carbon and life sustaining nutrients and oxygen throughout
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interactions/contacts. Monitoring and analysing contact pressure, surface contact distribution, and friction and movement patterns for personalised adjustments to equipment (and training). Based in both
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refine simulation tools and machine learning solutions to advance stroke treatment. This involves improving existing computational models that simulate cerebral blood flow, oxygen distribution, and brain
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duties involve supporting computational infrastructure, coordinating with wider spectroscopic project teams and external science users, contributing to documentation and user manuals, and collaborating
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memorisation capabilities of deep learning models. Such vulnerabilities expose FL systems to various privacy attacks, making the study of privacy in distributed settings increasingly complex and vital