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The Level A Research Fellow will contribute to the research efforts of the Department of Electrical and Computer Systems Engineering, with a specific focus on advancing a cutting-edge research program in
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades
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of the University may be required. Please review the position description for further information. About You You hold a doctoral qualification in Cognitive Neuroscience, Machine Learning, Computer Science or another
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and control of windfarms For this position the research activity is expected to develop new methods in robust and physics-informed machine learning, reduced-order modelling, data-driven nonlinear model
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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and
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-breaking research and innovative teaching. You can learn more about Adelaide University HERE and more information will be provided throughout the recruitment process. Enjoy an outstanding career
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) and strong general computer literacy, including the ability to quickly learn and use a range of research, administrative, and collaboration tools. Proven ability to work collaboratively within a team in
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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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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
and Host Area The School of Computing has a strong foundation in computing and information sciences at ANU. We are a transformative centre for research in artificial intelligence and machine learning
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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