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Postdoctoral Researcher in Quantum Information Theory who has: a PhD in Physics or a related discipline (or near completion) a strong background in quantum information theory or related areas the skill
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languages (C, C++, C#, Python, Matlab), experience with machine learning in robotics or computer vision, a desire to advance resilient, introspective processing architectures in robotics, a desire to work
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Carbon Modelling. This position offers an exciting opportunity to make use of the recent developments in AI and machine learning algorithms by measuring soil properties rapidly over a large area using cost
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internationally competitive research from molecules to medicines, from patient to policy across a range of therapeutic areas. The Charles Perkins Centre is a large multidisciplinary initiative committed to easing
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as required. About you PhD in a relevant field advanced skills in imaging data analysis, statistical analysis tools, and research reporting. experience in research, data capture, analytical skills, and
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double-edged sword” funded by the Australian Research Council. The project will use state-of-the art computer models that incorporate the latest frameworks on soil organic matter interacting with plant
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responsible for designing and implementing novel phenotyping protocols, utilising drone and hyperspectral imaging technologies to capture high-resolution phenotypic data and analysing large datasets to uncover
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science and lipids. Experience with AI and machine learning is preferrable but not mandatory experimental skills on microfluidics, analytical techniques, microscopies (fluorescent, confocal and electron
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on the ground robot for real-time soil property assessment, providing critical below-ground data to complement above-ground phenotypic analysis develop machine learning algorithms for real-time analysis of plant
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oscillations, delta Scuti stars, exoplanets, stellar clusters and associations, and machine learning. We make extensive use of data from NASA’s Kepler,TESS and JSWT Missions, and also have access to ground-based