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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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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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perspective to fundamentally solve the central question: how should an observer act in an environment to actively uncover the goal of the agent? Required knowledge Proficiency in Programming, Bayesian
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networks, Bayesian inference, computational neuroscience, mathematics.
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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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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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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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engineering and a strong foundation in data science. You bring a passion for solving complex problems and a track record of research excellence in optoelectronic materials, machine learning, or related fields
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, with access to a dynamic research environment connected to Monash Australia and Monash Malaysia. Candidate Requirements The successful applicant will have an excellent academic track record in a relevant