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Field
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Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs will also be provided Overview Edge artificial intelligence (Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI...
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PhD project: Modelling Resilience of Water Distribution Networks Supervised by Rasa Remenyte-Prescott (Faculty of Engineering) Aim: To develop an modelling approach for assessing water network
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Distributed radar systems comprise a coherent network of spatially distributed sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation
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Almost all radar systems currently transmit from the same location. A drastic departure from this sensing architecture is distributed radar – enacted by a coherent network of spatially distributed
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determined by combining the observed space density of galaxies, the measured spatial distribution of galaxies and simulations of the dark matter distribution. Example themes for student projects follow and
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very local measurement compared to the spatial extent of groundwater flow models. It is therefore interesting to combine these local measurements with distributed measurements of fluxes that can reveal
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lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
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subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal
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impacts of invasive insects on native biodiversity, human health, and food supply. The PhD candidate will mainly work on modelling species distributions and population dynamics of invasive insects such as
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feature spaces to find out-of-distribution atomic environments Use chemical neighborhood representations to detect sparse or unseen cases Combine ML features with chemical descriptors to enhance uncertainty