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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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increases bed scour and enhances vertical mixing, resulting in elevated suspended sediment concentration (water turbidity) and the generation of sediment plumes. Understanding the distribution and intensity
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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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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ultraprecise clock distribution (QT Mission 4: positioning, navigation, and timing; QT Mission 5: network synchronisation) and practical quantum sources for hybrid networks (QT Mission 2: quantum communications
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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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Project Overview This is an opportunity to conduct fully funded interdisciplinary research under the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex
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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