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Field
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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maintenance. However, current technologies are relatively slow and not capable enough to provide quick performance, diagnostic and prognostic predictions for real time applications. With the rapid development
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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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details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
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, currently requiring sufficient batteries or periodical recharging (and redeployment) from an expensive research vessel. To reduce the emissions and the costs associated with these vehicles, you will explore
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will contribute to the field by: Developing a conversational AI interviewer capable of conducting real-time adaptive interviews. Building an automated candidate ranking model based on interview
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process conditions. Furthermore, this research will focus on the development of a model, allowing for virtual testing and optimisation of the chemical recycling process. This includes potential
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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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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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undoubtedly of clinical utility in diagnostics and therapeutics. Current practices, in research and clinic, for protein sampling are invasive (blood, plasma or serum samples are used due to their protein