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Grant and offers excellent training in experimental techniques, data analysis, and translational research. The successful candidate will gain in-depth expertise in both cardiovascular and neurological
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operation under reactive capture conditions. Unravelling reaction mechanisms and catalyst structure-performance relationships using advanced characterisation techniques and computational tools. Collaborating
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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upcycling of spent metal oxide cathodes, particularly, high Ni-containing oxides, through chemical-mechanical approaches, ionothermal/molten salt methods, etc. Project 2 (2 PhD students): Direct Recycling
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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in biomedical engineering, mechanical engineering, or a related field. Experience in finite element modelling, biomechanics, or orthopaedic research is desirable. Strong analytical and problem-solving
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train a student in experimental electrochemistry techniques to enable their employability in academia, industry or other areas requiring such research skills. The student will be involved in research
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. The candidate will contribute to the development of empirically validated methods for identifying and mitigating such effects. The research will involve experimental studies, neurophysiological methods (e.g., eye
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will involve a novel comprehensive approach, including analyses of cellular fate such as reduction/oxidation in diverse biological fluids, tumour cells and tissues, the transport into and within cells
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of the complex non-Newtonian fluids present in this system. This Project addresses a significant scientific knowledge gap in the understanding of the rheology and thermal behaviour bulk plastic melt-flows