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disciplines of composite mechanics and electrochemistry, presenting compelling technical challenges and offering exciting opportunities to develop and innovate with new materials. These composites will provide
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to understand and develop a practical, method‑by‑method guideline that support statisticians in the design and analysis choices of SMART trial. This includes: Approaches to sample size calculation and design
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inclusive society programme. The project aims to address key challenges on enhancing climate resilience and sustainable development in rural and small urban communities by implementing transformative nature
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at the intersection of theory, computation, and high‑fidelity simulation, the successful candidate will contribute to the development of a novel ensemble-based framework for analysing driven perturbations in wall
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carried out under the UK Hypersonics Doctoral Network. This network is supported by the Ministry of Defence and EPSRC for building the necessary expertise to develop next-generation hypersonic vehicles
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co
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primary supervision of Dr Thomas Ouldridge. The student will develop predictive models of nucleic acid strand displacement rates to allow the rational design of complex networks of ever-increasing
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, which frequently produces designs that are suboptimal when subjected to real-world dynamic environments. Although a handful of advanced, high-fidelity solvers have been developed to tackle this issue
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must however ensure responsible use of lubricants by minimising its impact to the environment. This experimental project is centered around the development of novel water-based lubricants for EVs and