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to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity Join Australia’s
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig economies and prediction markets. Opportunity to supervise research students and work as part of a
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, and contributing to strategic objectives. The role enables you to work both independently and collaboratively, supporting the development of research partnerships, supervising emerging researchers, and
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approaches. Operating under broad direction, you will translate research objectives into specific experimental and analytical tasks, acting as a subject matter expert on methodological approaches and providing
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education and research. Discover more via our website. Your next career opportunity As a Postdoctoral Researcher in the ARC-funded Discovery Project "Connecting the Dots: the Mathematics of 4d/2d Duality
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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
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an understanding of the project life cycle and development of project plans, objectives and documentation. Excellent written and verbal communication skills, with a high level of attention to detail and the ability
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interact with a diverse range of stakeholders (lived experience collaborators, general public, policy makers) and students. An understanding of and commitment to UNSW’s aims, objectives and values in action
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure