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Join an ARC-funded research project developing numerical methods to simulate the flow of dense viscous liquids through a deformable granular mush. The project aims to improve fluid-dynamical
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project developing numerical methods to simulate the flow of dense viscous liquids through a deformable granular mush. The project aims to improve fluid-dynamical understanding of how magmatic ore
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intersection of fundamental discovery and real-world impact, the role offers opportunities to collaborate with quantum computing companies, application-domain partners, Queensland research institutions, and
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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systems develop connections between the Maslov index and the Evans function investigate fast-slow decompositions using geometric and asymptotic methods apply analytical and numerical methods to
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electrolyser testing. Demonstrated knowledge/experience in some numerical methods such as lattice Boltzmann method, finite difference (element, volume) method, phase-field method. Demonstrated programming
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function investigate fast-slow decompositions using geometric and asymptotic methods apply analytical and numerical methods to travelling-wave stability problems develop independent research
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scientific / numerical programming and data analysis excellent collaboration skills, with a proven ability to work effectively in a multidisciplinary team a keen, self-motivated interest in exploring novel
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computing Strong programming and data-analysis capability, preferably including Python and C/C++ A record of high-quality research outputs commensurate with opportunity and demonstrated ability to