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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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particular focus on flexible molecular crystals. Undertake molecular synthesis, crystal growth and advanced characterisation, develop and implement diffraction methods, including at major facilities, and
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imaging datasets. Strong technical knowledge across the data lifecycle, including research data architecture, data pipelines, database management, SQL, REDCap, cloud or high-performance computing
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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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rural communities. Working alongside leading researchers in a supportive, inclusive and innovative environment, you will play a key role in advancing high-quality mixed-methods research, fostering