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the effectiveness of an audio/visual online coaching programme that leverages digital health technologies as part of a novel intervention that can be easily accessed by a wide audience. This will be compared with
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Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
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supervisors spans five departments at University of Nottingham including Architecture and Built Environment, Electrical and Electronic Engineering, Mathematics, Physics and Social Sciences. The PhD programme
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/ physics/ meteorology/ engineering/ biology. Experience in a computer programming language (e.g. Fortran, Python). A general understanding of the importance of the scientific method is essential. A previous
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supervisors spans five departments at University of Nottingham including Architecture and Built Environment, Electrical and Electronic Engineering, Mathematics, Physics and Social Sciences. The PhD programme
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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of full-time study. Eligibility: The candidate should have a good 2.1 Bachelor's, or Master's degree in Electrical Engineering, Computer Science, Physical Sciences or equivalent. Experience in one
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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edaphic conditions in this island group. Plant material is already available so the focus is on laboratory and computing techniques. During the placement you will be taught DNA extraction, DNA data handling
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Background Earthworms are synonymous with our notion of beneficial soil biology playing a key role in maintaining a healthy functional soil. Earthworm populations are estimated to have declined by a