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Australian National University | Canberra, Australian Capital Territory | Australia | about 3 hours ago
research-intensive institution, the University values academics dedicated to solving problems through both applied and big-picture research. The Research Assurance Team, within Research and Innovation
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Full time continuing role Lead and shape the Student domain within the Division of the Academic Registrar Located on the Camperdown Campus. Offering a competitive remuneration package including 17
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basic and advanced light microscopy imaging. The Senior Microscopist will also develop application protocols to ensure quality use of instrument data and provide expertise and advice in sample preparation
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English Language Centre, which is a recognised leader in the higher education space for its English teachings and offerings. The Division of Academic and Student Engagement (DASE) partners with students, staff and
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, digitally enabled, and aligned with student aspirations and government and industry demands. You will closely collaborate with six higher education schools and our vocational education and training division
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% superannuation & leave loading Location: Kensington NSW (Hybrid Flexible Working) About Division of Societal Impact, Equity and Engagement (DSIEE) The UNSW Division of Societal Impact, Equity and Engagement
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. The role also contributes to the broader operations of Student Administration and Library Services by supporting the efficient, effective and timely delivery of services across the division as required
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to apply. Who are we looking for? We are seeking a Senior Research Engineer (Robotics & Autonomous Systems) to join Research Infrastructure, Research Portfolio, Academic Division. The Senior Research
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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment