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to train tomorrows 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 continuously learn, adapt, and refine world models in self-adaptive and autonomous systems. Specifically, the research will investigate how AI-based methods can support the evolution and updating of transition
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to continuously learn, adapt, and refine world models in self-adaptive and autonomous systems. Specifically, the research will investigate how AI-based methods can support the evolution and updating of transition
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Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about
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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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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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, Science and Economy, to commence on 1 January 2026 or as soon as possible thereafter. This PhD project aims to develop advanced, climate change resilient road gully grating designs that enhance efficiency