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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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at the intersection of causal learning, inference, and deep learning, leveraging Graph Neural Networks (GNNs) and Large Language Models (LLMs). The successful candidate will explore how GNNs can model causal structures
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learning is vulnerable to spurious correlations, novel causal discovery and inference methods will be developed to identify and reason over causal relationships among all associations from fused data. As the
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numbers of minimally-interpretable models being used, as opposed to traditional models like decision trees, or even Bayesian and statistical machine learning models. Explanations of models are also needed
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physics, quantum engineering, or related area. Demonstrated track record of operating and measuring nano-mechanical devices that display quantum behaviour. Demonstrated track record of designing and
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successfully completed unit NSB233 and being on track to enrol for NSB334 in Semester 1, 2026 the final year of NS89 Master of Nursing – Entry to Practice in Semester 1, 2026 This includes successfully completed
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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
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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
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the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms
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. Among the approaches used will be the Bayesian information-theoretic Minimum Message Length (MML) principle (Wallace and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) References: Wallace, C.S