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About the Project The future power grid will be a highly complex cyber-physical system, integrating multiple distributed energy resources (DERs) such as solar, wind, marine, and bioenergy alongside
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into volatile chlorides. The process efficiency, however, depends on a complex interplay of particle-scale interactions and particle/solid body interactions. Current industrial practice relies heavily
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mass production. For training purposes, the student will reproduce selected literature results before moving on to systems of their own design. They will design, build and characterise complex membrane
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-of-the-art AI and computing facilities, receive tailored training and professional development, collaborate with experts across disciplines, and contribute to open-source tools that advance the wider AI
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autonomous systems. You will have the freedom to shape the project direction, experiment with novel ideas, access state-of-the-art AI and computing facilities, receive tailored training and professional
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necessarily require formal education in geotechnics. Applicants with a background in mechanical/materials engineering or alternatively mathematics/computer science with an interest in numerical modelling
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science including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic
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propagate through bacterial communities while deactivating AMR genes. However, current designs are limited by scalability and complexity. This project aims to overcome these limitations by integrating large
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benefit from world-leading infrastructure uniquely suited to support the programme, i.e. a fully operational network of Commercial-off-the-Shelf (COTS) primary surveillance radars specially modified
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this goal. However, the computational expense of these models limits their use for generating forecasts, constraining the spatial resolution, level of physical complexity, and number of ensemble members