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for Machine Learning (AIML) is the largest university‑based machine learning research group in Australia and the country’s first institute dedicated to advancing machine learning, computer vision, deep learning
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to equity, excellence and impact. VU’s vision, outlined in our strategic plan Start Well Finish Brilliantly (2022–2028) , is to be a global leader in dual-sector research and learning by 2028. Our strategic
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radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these approaches in radiation therapy
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or implementing deep learning approaches on existing clinical systems). Experience and interest in grant writing would be viewed favourably. To be successful in this position, you will have: A PhD in a relevant
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of published research relevant to clinical medicine, especially those using quantitative research methodologies. Experience and demonstrated achievement in University-level teaching and learning. A record of
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understanding, anticipating, and managing risks posed by harmful algae to seafood production, public health, and consumer confidence. The successful applicant will have a PhD and demonstrated expertise in seafood
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in the fields of synthetic biology, photonics, and quantum technologies. More details about the SiP Lab can be found at: https://www.mq.edu.au/faculty-of-science-and-engineering/our-research/silicon
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will have completed a PhD in biochemistry, molecular biology, microbiology, structural biology, synthetic biology, or a closely related discipline. You will enjoy contributing to impactful research
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journals, Supervision of PhD and undergraduate research students Make use of ice sheet models, machine learning and published field datasets to reconstruct the evolving post-LGM Antarctic Ice Sheet. Initiate
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track record of developing and deploying state-of-the-art AI solutions. CHI is a research centre that drives digital health forward and shapes the future of AI in healthcare. https://www.mq.edu.au/__data