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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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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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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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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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. 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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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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team at Swinburne University’s Department of Mechanical and Product Design Engineering. This PhD will provide a unique opportunity to conduct impactful research with real-world industry applications
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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
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This project seeks to pinpoint cellular and molecular epigenetic mechanisms that drive age-related immune-senescence and inflammation, critical to the decline in immune function and ability