505 web-programmer-developer-"https:"-"https:"-"https:"-"UCL"-"UCL" PhD positions in United Kingdom
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the wind/renewable energy training and research elements of the CDT programme. Funded by ESB and EPSRC, this 4 year this PhD studentship, at the University of Strathclyde is in the area of structural
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explaining issues clearly or prioritising them by player impact. This collaborative doctorate (University of Surrey, SAHCI, and Sony Interactive Entertainment, SIE) will develop a QoE-driven agentic AI
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Programme, which seeks to transform the UK’s capabilities to improve understanding of mechanisms and health outcomes, using respiratory and allergic disorders as exemplar domains. We seek to enrol the most
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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for devices containing quantum materials. The project addresses this gap by developing measurement protocols which test the Hamiltonian hypothesis and hence support the materials discovery process. [1] E. Lupo
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due to a high Stoke’s shift, and a fast response time. In this project we will develop prototype perovskite scintillator gamma detectors fabricated from perovskite materials grown at Surrey. Perovskite
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assemblage changes. Determining how coral reef fish food webs and energy pathways are connected along extended depth gradients of 0-90 meters. Based in Newcastle, and with international supervision, you will
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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance
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operability and performance. The project will build on previous successful programmes which have developed considerable expertise in the measurement of intake flow distortion using non-intrusive methods
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with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large