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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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is known as 'Blackbox Multi-Objective Optimization for Unknown Functions', which will help the users (e.g., scientists) to explore the input space of their experiments (i.e., x) that maximizes
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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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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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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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an attractive remuneration and relocation package. 1. Professor, Monash Health and Climate Initiative We are seeking a distinguished academic with an outstanding track record in climate change and health
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reinforcement learning. In International conference on machine learning (pp. 2107-2128). PMLR. - Péron, M., Becker, K., Bartlett, P., & Chades, I. (2017, February). Fast-tracking stationary MOMDPs for adaptive
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them The Opportunity Lead Innovation at the Edge – Shape the Future of Rail Technology Join the Institute of Railway Technology (IRT) and lead the development of advanced condition monitoring solutions
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experience overseeing budget, financial and resource planning to drive institutional efficiency and achieve organisational objectives A track record of executive-level leadership, with proven success in
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that align with the Centre’s mission and revenue growth objectives. You will proactively engage with industry leads to understand their needs, present tailored solutions, and convert opportunities