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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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critical, innovating at the boundaries of their disciplines and creating exciting and engaging learning experiences for our students – the global citizens of tomorrow. Design is a rapidly growing and highly
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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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) 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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focused) who possesses: PhD in interaction design, human-computer interaction or cognate field sound experience of teaching and learning, and the ability to contribute to teaching at undergraduate, honours
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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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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
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superior computer programming or software development skills good communication skills (both written and oral) potential to design and engage in high-quality teaching and learning experiences (in person and
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thrives in a multifaceted environment. You will not only deliver a comprehensive curriculum encompassing software development, machine learning, computer systems, and networks, but also be instrumental in
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thrives in a multifaceted environment. You will not only deliver a comprehensive curriculum encompassing software development, machine learning, computer systems, and networks, but also be instrumental in