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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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how variations in mould structure, porosity, and surface characteristics affect radiative heat transfer and casting performance. Phase-field modelling will also be used to simulate defect formation and
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system
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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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process, and this process itself can impede certain policy. This project involves summarising models of political choice (e.g. the median voter, probabilistic voting, citizen candidate, etc models) with a
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satellites, with the potential for travel to test instrumentation in ideal locations. Additionally, the simulation work will focus on developing computational models to validate instrumentation and optimising
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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vulnerabilities. Frontier models show superior performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases human involvement is still required, and fully autonomous
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electroencephalography (EEG), behavioural tasks, and computational modelling, the student will examine how these interventions influence brain dynamics, cognition, and subjective experience.