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Apply now Job no:523884 Work type:full time Location:Sydney, NSW Categories:Post Doctoral Research Associate The Opportunity We are looking for machine/deep learning expert to join our team that is
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personal commitments, while optimising their work performance and contributions to the University. See how we lead in flexible work to enable an outcome focused and inclusive workplace. To learn more about
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commitments, while optimising their work performance and contributions to the University. See how we lead in flexible work to enable an outcome focused and inclusive workplace. To learn more about our culture
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needs. Professor Kannan Govindan and Associate Professor Devika Kannan lead this new centre. Prof Govindan explores the use of industry 4.0 technologies (blockchain, big data, AI, machine learning, and
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needs. Professor Kannan Govindan and Associate Professor Devika Kannan lead this new centre. Prof Govindan explores the use of industry 4.0 technologies (blockchain, big data, AI, machine learning, and
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 22 hours ago
and machine learning, galaxy formation and simulations, integral field unit observations. Applications are encouraged from researchers who wish to undertake novel and collaborative research as part of
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spectroscopy and stellar abundances, stellar structure and stellar evolution, chemical evolution modelling, large survey data, statistics and machine learning, galaxy formation and simulations, integral field
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arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning – the largest computer vision and machine learning research group in Australia – and contribute
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of data analysis techniques (including machine learning) and process-based algorithms to diagnose and interpret high-frequency hydraulic data for the purpose of providing actionable information. Analysis
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. This position will involve the development of data analysis techniques (including machine learning) and process-based algorithms to diagnose and interpret high-frequency hydraulic data for the purpose