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Field
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build the sustainable companies and societies of the future. The EISLAB division of the Department of Computer Science, Electrical and Space Engineering conducts research within Cyber-Physical Systems
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the UKRI rate (£19,237 for 2024/25) and tuition fees will be paid. We expect the stipend to increase each year. Modernised distribution power networks face an unprecedented challenge as thousands of power
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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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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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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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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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team tackling major challenges facing the world’s population in global sustainability and wellbeing as part of the QUEX Institute. The joint PhD programme provides a fantastic opportunity for the most
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, particularly in computer networks, operating systems, computer architecture and distributed systems Excellent programming, system building and measurement skills are required Be familiar with, and ideally worked
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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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