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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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data sources to answer important population-level questions that will inform health technology assessment, policy, and practice. The successful PhD student will be embedded within an interdisciplinary
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– the Doctoral Landscape Award (DLA) and the Doctoral Focal Award (DFA) – as part of its Future Doctoral Programme. The new awards aim to provide more flexible, inclusive, and strategically aligned funding across
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Science and Technology Facilities Council (STFC), Scientific Computing Department Daresbury Laboratory, with the PhD being conducted between Bangor and Daresbury with a 50:50 time split. This project is
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integration into neural circuits to result in functional, restored behaviour. We will use the fruit-fly Drosophila as a model organism for its powerful genetics and molecular cell biology (e.g. CRISPR/Cas9 gene
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opportunity to network and work directly with them as part of you PhD programme. Number of awards: 1 Start date: September 2026 Award duration: 3.5 years Sponsor: ESRC JUST Supervisors The core supervision team
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language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with no less than 5.5 in each component test area) or equivalent
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap