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Australian National University | Canberra, Australian Capital Territory | Australia | 12 minutes ago
approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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experience in computer programming, machine learning, and deep learning, with evidence of practical application in research projects. Strong capability and analyse large datasets, particularly those involving
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. Experience with bioinformatics tools and libraries for genomics analysis (e.g., Seurat, Scanpy, CellRanger, Nextflow, Singularity, Docker). Expertise in machine learning techniques and deep learning frameworks
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geological data to create a tectonic, bathymetric, and topographic digital twin of the Earth’s surface from 1800–500 million years ago. This model will be used to explore how deep Earth processes—such as
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collaboratively with colleagues from multidisciplinary disciplines Excellent time management and planning skills, with a commitment to delivery Strong background in machine learning and/or deep learning, and signal
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, our aim is to be a global leader in dual-sector learning and research by 2028. Join us on the journey and help us achieve our strategic drives embedded in our Strategic Plan 2022-2028: Start well
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of Melbourne acknowledges the Traditional Owners of the unceded land on which we work, learn and live: the Wurundjeri Woi Wurrung and Bunurong peoples (Burnley, Fishermans Bend, Parkville, Southbank and Werribee
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. The candidate will have a PhD in Computer Science or Machine Learning (or be able to demonstrate equivalent research experience) and possess a deep and demonstrable knowledge of these fields. They must be a
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: Competitive track record with demonstrated high-quality publications. Research experience related to network data mining or recommendation models. Programming skills in deep learning or machine learning
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. Learn more about Monash . With excellent, high-quality students, a vibrant research environment, being based in the Suzhou Industrial Park, which houses over 100 Fortune 500 companies, and in close