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), psychophysical methods, computational neuroscience methods, machine learning and modelling. Our studies utilise various haptic devices, and mechanical and electrical stimulators. Key skills required: PhD
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in Deep Learning Theory who has: a PhD in mathematics, applied mathematics, data science, or a related area an excellent track record of publishing high-quality papers on deep learning theory, machine
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papers on deep learning theory, machine learning theory, or approximation theory research experience in deep learning theory, machine learning theory, and approximation theory a solid mathematics
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partners and stakeholders use mechanistic modelling to spatialise crop yield predictions under a variety of data input scenarios use data-driven models and machine learning to better understand the spatio
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Accuracy in Algorithmic Machine Learning"). Working closely with Dr. Clement Canonne and his team, the successful candidate will develop and analyse new differentially private algorithms for distributed
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Machine Learning”). Working closely with Dr. Clement Canonne and his team, the successful candidate will develop and analyse new differentially private algorithms for distributed statistical inference, and
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licence is essential. Car ownership is desirable. To learn more about this position, and view the comprehensive skills and criteria list, a copy of the Position Description can be found by clicking
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collaborator with the aim to use machine learning/AI approaches in combination with commercial multispectral and hyperspectral remote sensing platforms to perform within-field mapping of weeds and disease in
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within the Australian Centre for Robotics (ACFR ) at the University of Sydney. The ACFR is one of Australia's leading robotics research groups, and the Robotic Imaging Lab is focused on endowing machines
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) at the University of Sydney. The ACFR is one of Australia’s leading robotics research groups, and the Robotic Imaging Lab is focused on endowing machines with new ways of seeing the world. We are expanding our team