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Engineering (PhD) Eligibility: UK Students Award value: Home fees and tax-free stipend £20,780 - See advert for details Project Title: Machine Learning and Optimisation-Based Intelligent Substation Design in
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to investigate the conditions that support forest recovery after a fire. The study areas can be defined using...
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The Topic This PhD studentship is based in the School of Psychology at the University of Kent. The studentship is funded by a European Research Council Starting Grant fulfilled through a UKRI
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part of the larger project. The scope of the PhD project is to implement, use, and where required develop, statistical machine learning tools to identify DNA mutations that cause particular types
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability
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into car cabins (to reduce the exposure of occupants) and the regions where pollutants accumulate (to be avoided by pedestrians and cyclists) need to be minimised. The PhD objective is to study the effects
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol
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bottleneck in the screening process. This PhD project will address this through deep integration of scanning probe electrochemistry, optical microscopy and machine vision, to develop a system that can