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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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This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis
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. Butler, C. Goncu, and L. Holloway. Tactile presentation of network data: Text, matrix or diagram? In CHI2020, pages 1–12, 2020. I. Zukerman et al.˙Exploratory Interaction with a Bayesian Argumentation
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statistical, Bayesian, and deep-learning approaches. Lead improvements in data quality, integration, and reproducibility across multi-centre trials and registries. Collaborate with leading clinicians, engineers
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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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in Rail Technology – Join the IRT Team The Institute of Railway Technology (IRT) at Monash University is seeking a Senior Data Engineer to lead the design and delivery of high-performance, scalable
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968