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Field
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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ligand(s). The 3D organisation of these domains is therefore critical for their function. The object of our studies is to gain a fundamental understanding of this incredible family of glycans, opening
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
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alongside traditional coal fired power stations and nuclear energy generation. Revolutionary changes to power conversion is indispensable if these carbon emissions targets are to be met. The objective is to
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healing, haemostasis promotion and water treatments). The objectives of this project focus on evaluating the antibiofilm efficiencies of different Chitosan derivatives with various polymer sizes and to
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, commissioning cost, lifetime maintenance, replacement and disposal costs and environmental costs also add up to the total evaluated cost of a transformer. Different objective functions in terms of minimisation
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of work throughout the PhD. Project aims and objectives The primary aims of this PhD project are as follows: To investigate how dietary supplementation modulates the gut microbiota and gut permeability
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sensor spoofing. These attacks manipulate input data while maintaining apparent operational normality, potentially leading to unsafe decisions without detection. This project aims to develop a novel
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Behaviour-Based Anomaly Detection