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Research Fellow - Advanced Signal Processing and Machine Learning Techniques for Vital Signs Measurement from Video Images Job No.: 691722 Location: Clayton campus Employment Type: Full-time
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Research Infrastructure? No Offer Description Based at Australian Institute Machine Learning (AMIL), City East Campus 2 x Full-time, 2-year fixed term contracts Salary Range: $114,917 - $135,932 per annum
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on advancing AI technologies for space systems with significant national and global impact. Visit the AU website to learn more about the School of Computer and Information Technology . Our people Our people
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industry to translate innovation into practical solutions. Visit the AU website to learn more about the School of Computer and Information Technology . Our people Our people are guided by purpose, curiosity
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econometrics and machine learning, is highly desirable. You are well-organised and capable of managing multiple tasks to meet deadlines, with excellent written and verbal communication skills for producing clear
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, measure transport and machine learning on developing a novel mathematical framework for identifying reduced dynamical models of high-dimensional complex multi-scale systems. The project will develop fast
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lung health, across a range of laboratory and imaging programs. Role Summary This role will help to develop lung function-based respiratory biomarkers through AI, machine learning and image processing
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 19 days ago
machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades of computer science methodologies with modern data and
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discover them The Opportunity The Department of Electrical and Computer Systems Engineering at Monash University is seeking a motivated Level A Research Fellow for a 2 year research-only appointment. A Level
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly