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Field
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vision, mathematical physics, data science and quantum measurement. About the opportunity Support the development of scalable, customisable data platforms to enhance collaborative geoscience research
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techniques and associated tools (examples include, but are not limited to machine learning, density-functional-theory, materials informatics, finite-element modelling, phase-field modelling), and demonstrated
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Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data
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research activities, and engaging in teaching, student supervision, and mentoring in the areas of edge computing and machine learning. About You The successful candidate will hold a PhD in Computer
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
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to assist in the delivery of research and monitoring projects across Northern Australia. The successful applicant will work closely with Traditional Owners. What you can bring to the role Possess either a PhD
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. Selection Criteria Level A 1. A PhD in Remote Sensing, Plant phenotyping, Computer Vision, Machine Learning, or a related field. 2. Strong experience in image processing and computer vision, particularly
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on developing and implementing seagrass restoration approaches in tropical Australia and the broader Indo-Pacific region. What you can bring to the role Possess either a PhD, or a Bachelor’s degree with a minimum
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@rmit.edu.au Dr. Shao, Wei (Data61, Marsfield) - wei.shao@data61.csiro.au The successful candidate is expected to have strong motivation and evidenced skills in machine learning and computer vision