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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Please contact the supervisor, Prof Matt Gibson (matt.gibson
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related discipline. Please contact the supervisor, Prof Matt Gibson, for this project before you apply. You should include a 2 page (max) CV in your email, including grades, details of subjects studied and
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details of the project informally, please contact: Prof Hywel Morgan, Digital Health and Biomedical Engineering Research Group, Email: hm@ecs.soton.ac.uk Entry Requirements A very good undergraduate degree
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generated high-fidelity LES datasets. These datasets, produced under a parallel investigation, will provide detailed flow and temperature information for a geometry representative of a pebble-bed HTGR core
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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machine learning are encouraged to apply. If you wish to discuss any details of the project informally, please contact Prof Suan Hui Pu, Smart Manufacturing and Systems Research Group, Email: suanhui.pu
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desirable as would experience integrating behavioural, phenomenological and neuroimaging datasets with theoretical implications. Diversity Committed to equality and valuing diversity Application Process You
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to collaborate not only with peers in Swansea but also with partners across the UK fostering a rich and diverse research environment. Your role will be to develop robust remanufacturing process
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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may apply stricter criteria to certain nationalities that may be over-represented in Singapore. As part of the offer/nomination process, The University of Manchester will select the most outstanding