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Apply now Job no:523884 Work type:full time Location:Sydney, NSW Categories:Post Doctoral Research Associate The Opportunity We are looking for machine/deep learning expert to join our team that is
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personal commitments, while optimising their work performance and contributions to the University. See how we lead in flexible work to enable an outcome focused and inclusive workplace. To learn more about
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commitments, while optimising their work performance and contributions to the University. See how we lead in flexible work to enable an outcome focused and inclusive workplace. To learn more about our culture
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learning or closely related field Prior experience in designing and training diffusion models A track-record of high-quality research in computer vision or machine learning First-author publications in
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information. federated learning, whereby a machine learning model can be trained by using data from different clinical sites but without requiring central data aggregation. The data used in this project will
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University of Kansas Department of Civil, Environmental, and Architectural Engineering | Virginia, Northern Territory | Australia | 39 minutes ago
The Civil and Environmental Engineering Department at Virginia Tech is seeking a Postdoctoral Researcher with expertise in hydrology, water quality, machine learning, and eXplainable AI
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Accuracy in Algorithmic Machine Learning"). Working closely with Dr. Clement Canonne and his team, the successful candidate will develop and analyse new differentially private algorithms for distributed
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of operations of radio frequency equipment emerge in increasingly complex, congested and contested spectral environments. With recent advances in machine learning across multiple fields, the time is ripe for a
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Machine Learning”). Working closely with Dr. Clement Canonne and his team, the successful candidate will develop and analyse new differentially private algorithms for distributed statistical inference, and
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collaborator with the aim to use machine learning/AI approaches in combination with commercial multispectral and hyperspectral remote sensing platforms to perform within-field mapping of weeds and disease in