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computer vision and machine learning research group in Australia -- and contribute to world-leading research projects at the CommBank Centre for Foundational AI This postdoctoral research position is part of
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the direction of A/Prof Claudia Szabo in the School of Computer and Mathematical Sciences at the University of Adelaide. The project is a collaboration with Defence Science and Technology Group, within the Combat
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Capability is a $250 million enterprise powered by UoA and UNSW, with funding from the Australian Government through the Trailblazer Universities Program, as well as university and industry partners. Our
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) Based at our Herston Campus . About This Opportunity Join a nationally significant program driving innovation in clinical trial methodology at one of Australia’s leading research institutions
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Australian National University | Canberra, Australian Capital Territory | Australia | about 1 month ago
approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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only internationally accredited field epidemiology training program. Work with the MAE team to integrate diversity, equity and inclusion into all aspects of the MAE Program, including the curriculum
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Take a leadership role in the development of a multi-strain Streptococcus pyogenes computational transmission model Full Time/2 Year Fixed Term Research Only Appointment, Remuneration at Academic
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with concrete real-world applications Engagement with Defence Science and Technology Opportunity to hone supervision skills. Position Overview The Robotics group at the ANU School of Computing is opening
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activities draw together academic, research and professional staff from a variety of science, engineering, and computing disciplines to tackle unique and demanding challenges in radio astronomy and beyond
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experience contribute to ongoing translational research program related to the application of statistical and machine learning methods in reproductive and perinatal medicine using both clinical quality