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Field
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motivated PhD candidate with interests and skills in computational modelling and simulations, fluid dynamics, mechanical engineering, physics and applied mathematics. You should have experience in one or more
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, memory, and energy requirements. The successful candidate will explore novel algorithms and model-design strategies that allow AI systems to operate effectively on edge devices, clinical environments
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pilots to real-world systems. The overarching aim is to deliver a scalable approach, pairing shared “aggregator” models with household-specific “client” models that exchange knowledge while keeping data
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systems, providing early detection of adverse events such as infection and inflammation. The project will involve sensor design and modelling, prototype development, electrochemical characterisation, and
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analysis, threat modelling, and proof-of-concept demonstrations, the research will identify and map vulnerabilities that may impact both individual investors and global financial stability, as
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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for significant improvements in the fields of finance investment management, and decision-making. The project will also aim to provide suitable benchmarking methods to evaluate our proposed AI/NLP/LLM models
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factors, including geographic location, hydrodynamic conditions, and water depth. This PhD will build upon numerical modelling studies to employ physical modelling experiments in state-of-the-art facilities
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. Approach and Methods: Apply deep learning-based modelling and clustering to analyse a curated dataset of hundreds of thousands of UL-CDR sequences Characterise sequence–structure relationships and structural
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carbon fibre reinforcement. This is a complex thermos-chemical-flow process which is difficult to model and to monitor which has a major impact on production time and product quality. We have developed