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Field
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characterisation and radionuclide partitioning using specialised experimental facilities within the NNUF RADER facility (https://www.nnuf.ac.uk/rader ). This will include the use of state-of-the-art facilities
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studies, guide people through immersive experiences and collect behavioural and emotional data. Because of this, the project would suit someone who is confident in people-facing situations and enjoys
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information in the Information package for Marie Curie fellows in doctoral networks (https://op.europa.eu/s/z831 ). Selection process For the selection procedure, the SLICE consortium will appoint a
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to the delivery of the Chair’s programme. More information on the UNESCO Chair in Storytelling Education for Sustainability can be found here: https://www.lboro.ac.uk/schools/design-creative-arts/unesco-chair
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modelling techniques and the embedding of such models within data assimilation frameworks to enable their self-correction. During this project, you will develop expertise in machine learning-based reduced
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resilient. The PhD student will take the lead on the full research programme: designing studies, analysing data, developing models, and co-producing interventions with DfT and other partners. The PhD student
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conferences. You will have access to engaging professional development workshops in areas such as research communication, computing and data science, and professional progression through our Early Career
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in Experimental Particle Physics (https://www.hep.manchester.ac.uk/study/ ). Our group (https://www.hep.manchester.ac.uk/ ) is one of the largest research groups in the UK with over 100 members
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requires weekly travel to attend in-person training at these universities, which will be covered by the CDT. For further information about the CDT programme, please visit the CDT website at www.fusion
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behavioural experimental design and statistical modelling; computer vision and AI techniques; explainable AI and human–machine comparison methods; and responsible innovation. The student will work closely with