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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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Honours degrees in the following disciplines, or with equivalent research or work experience will be favourably considered: Computer and Data Science; Applied Mathematics and Statistics. Number
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supported by an ARC Industry Fellowship, in partnership with Bush Heritage Australia. The student will work closely with ecologists and computer scientists at QUT and conservation managers at Bush Heritage
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experienced supervisors, each with over 20 years of expertise in machine learning and computer vision. These supervisors have strong track records of research excellence, with numerous publications in top-tier
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; trying to understand why certain elements are more abundant than others; or how the different populations of stars in globular clusters arose. How can we better approximate mixing during core He burning
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spectroscopy and Gaia data of star clusters to decipher the mystery of the Lithium-rich giant stars" (with Prof John Lattanzio) "The origin of the heavy elements: Computer simulations of neutron-capture
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of people with disability. These might, for instance, utilise conversational agents, computer vision, mixed reality, wearables etc. Disability, Technology, and Society: Research with a sociological or
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and cost outcomes, which include but are not limited to Statistical (risk) modelling, Model calibration and uncertainty estimation, Causal learning for explainable machine learning, Transparent
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply