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engineering excellence needed for the aerospace sector. In this PhD, high-fidelity two-phase Computational Fluid Dynamics (CFD) methods will be used to model complex and fundamental cryogenic hydrogen flows
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high 2:1 in computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and
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prepared for changes to your work duties after employment. Required selection criteria You must have a relevant Master's degree in computer science, automation related engineering or equivalent. Your
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Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career
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under the supervision of Dr Mehrdad Vasheghani Farahani from Department of Chemical Engineering, with co-supervision by Professor Ian Kinloch from Department of Materials. The successful candidate will
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Candidate, it is a prerequisite that you gain admission to the PhD programme in Engineering within three months of your employment contract start date, and that you participate in an organized doctoral
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CD3 is a new, multidisciplinary and multi-institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception
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for QUT's Doctor of Philosophy , including any English language requirements. Enrol as a full-time, internal student. Have a background in electrical, mechatronic, or biomedical engineering, expertise in
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including the Ground Operations Laboratory in the Digital Aviation Research and Technology Centre (DARTeC), and the suite of facilities in the new Cranfield Hydrogen Integration Incubator (CH2i
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at the intersection of theory, computation, and high‑fidelity simulation, the successful candidate will contribute to the development of a novel ensemble-based framework for analysing driven perturbations in wall