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Field
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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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of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological
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horticultural data from digital images by analysing their content. The aim is to infer information that might not be immediately apparent, even to the photographer, in order to improve our understanding of animal
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polycrystalline material during plastic deformation in order to eventually predict the manner in which materials deform and fail. As a first step, we wish to infer a distribution of the directions of deformation
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networks, Bayesian inference, computational neuroscience, mathematics.
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. This will be achieved through frequency domain and time domain state and parameter estimation techniques to infer model states and parameters in real time to simultaneously track the anaesthetic brain states
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Masters project Supervisors Login Recently added Development of a GIS-Based Model for Active Citizenry Street-Level Environment Recognition On Moving Resource-Constrained Devices Bayesian Generative AI (PhD
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/electrical-engineering-telecommunications Skills & Experience Research Associate (Level A) PhD (or soon to be awarded) in quantum physics, quantum engineering, or related area. Demonstrated track record
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on Artificial Intelligence and Statistics (AI+STATS 2001), pp253-260, Key West, Florida, U.S.A., Jan. 2001 P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic
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Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum