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, Level A/B $86,584 p.a. - $122,830 p.a. + 17% superannuation About the opportunity Are you excited by the opportunity to apply machine learning techniques to discover new solutions to human health? Early
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, machine learning, remote sensing and silviculture to transform and upscale forest phenotyping operations. The role will be supervised by Dr. Mitch Bryson, who currently leads the Forestry Research group
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the university and faculty/school strategic initiatives and undertake related projects About you Bachelor’s or Master’s degree in computer science, data science, machine learning or a relevant field
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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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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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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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to grant applications and submissions supervise postgraduate and honours research students co-ordinate various administrative tasks associated with research projects. have machine learning and programming
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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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) 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
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literacy with a good knowledge of relevant computer programs, and ability to work across and connect these as needed. Desirable criteria – you may have (or be willing to learn) the ability to write research