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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 hours 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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Australian National University | Canberra, Australian Capital Territory | Australia | about 19 hours ago
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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, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
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and any students who may be assisting with the research. Deal with problems that may affect the achievement of research objectives and deadlines. Promote equality and values diversity acting as a role
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
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Supervised Machine Learning and Reinforcement Learning. The objective is to significantly enhance battery performance and longevity. While conventional methods rely on either physics-based models or high-level
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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
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can be tackled. A video describing the project can be viewed here: https://www.youtube.com/watch?v=IzPuuBnrIDc . The successful candidate will be developing Bayesian models for estimating
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contributing to more trustworthy and robust inferences. In specific, the candidate will: Combine formal Bayesian theoretical connections with quantitative experiments to develop methods for quantifying