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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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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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: Callaghan Campus About the Role As a Lecturer in Computing and Information Technology (Teaching Focused), you will play a key role in delivering high-quality teaching and creating engaging learning
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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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with our communities to discover, create and innovate with sustainability at our core. THE ROLE The School of Computing is inviting expressions of interest for inclusion within our casual academic
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activity ranging from physics to materials science, acoustics, micro- and nanotechnology. We offer reservoir computing measurement setup, high frequency NEMS characterization platform, numerical simulators
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Inria, the French national research institute for the digital sciences | Villeneuve, Queensland | Australia | 22 days ago
algorithms . Incorporate partition crossover and related tunneling mechanisms into specialized evolutionary and local-search algorithms, especially by exploiting parallel and high-performance computing
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new codes and high-performance computing work with state-of-the-art codes, including direct numerical simulation, large eddy simulation and probability density function methods mentor research