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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how environmental factors and antifouling technologies
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of biofouling processes in marine environments. This role will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 24 days 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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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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, mathematical psychology (computational modelling) and/or human factors methods and related statistical techniques (including Bayesian hierarchical methods) Experience with the development and application