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of Electrical and Mechanical Engineering has an outstanding reputation for teaching and research excellence in the areas of electrical and electronic engineering, computer engineering, communication systems
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the forefront of cancer systems biology, uniquely combining computational and predictive modelling with experimental approaches to tackle major challenges in cancer treatment. Our innovative research spans from
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the timing, scale, and rate of mammal declines in Australia. They will use critical inferences of past demographic change and high-performance computing to disentangle the ecological mechanisms that were
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, with a focus on elucidating interfacial components and reaction mechanisms at the fundamental level. Potential to lead a research program in fundamental and/or applied areas addressing current battery
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the SAiGENCI Cancer Epigenetics program and ACE strive to unravel the epigenetic mechanisms contributing to cell fate, development and cancer, with the aim of developing effective therapeutics. The postdoctoral
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negotiable, depending on qualifications and experience. The School of Electrical and Mechanical Engineering is seeking applications for the Ian and Pamela Wall Endowed Chair Professor in Electrical and
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cover core engineering and technology subjects including Mechanics Technology, Systems and Complex Systems Engineering, Programming (Matlab, C and Excel), Business Management Studies, Research Methods
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splitting and C–N coupling reactions. Work includes computational modeling of carbon-based materials, conducting simulations to understand reaction mechanisms, and developing and applying machine learning
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associated with subsurface H2 and CO2 storage in complex geological settings. Key foci of the experimental research program that the PDRA will undertake include: identifying secure sites for subsurface H2 and
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. This project will use state of the art MRI sequences to characterise the morphology and composition of human joints for a cohort of people enrolled in a social running program, and subsequently phenotype