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One of the essential networks for society is water distribution networks. The delivery of water to customers is affected through different threats to the system. These include failure of components
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). Overview This PhD will develop a data-driven framework to understand how climate-driven extreme weather affects electricity distribution networks and how targeted interventions can reduce disruption. Using
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), such as solar photovoltaics (PV), electric vehicles, heat pumps, and storage systems, into distribution networks. Delivering this transition requires coordinated innovation across both active distribution
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science or systems engineering. Knowledge of AI/ML algorithms, particularly graph neural networks and reinforcement learning, is highly advantageous. A keen interest in distributed computing, IoT architecture, and
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There is growing UK and international interest in networked sensing and autonomous collaborative platforms, where multiple airborne sensors co-operate to collect and exploit data. In contrast
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LIBS can provide advantages beyond the complementary information that the two techniques can offer. While LIBS gives quantitative and distribution information of light and bulk components, LA-ICP-MS can
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consequences of mineral extraction in the transition to net zero. Despite the global push toward decarbonisation, communities living in mineral‑rich regions often face exclusion from decision‑making, inequitable
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economy-wide modelling. Assess the distributional impacts of network investment using macroeconomic models such as Input Output and Computable General Equilibrium. There is a unique the opportunity to gain
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Research area and project description: AI data centres are digital engines, yet ~30% of energy is wasted as heat in power conversion and distribution. Directly addressing the UK’s Clean Power 2030
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, and battery storage are deployed at scale. These technologies are essential for achieving climate targets, but they also place unprecedented stress on local electricity distribution networks, which were