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viewpoint biases/opinions and support epistemic uncertainty. Can we disentangle the biases/opinions of a diversity of sources? We could adapt Bayesian meta-reasoning and work with the LLM in an agentic
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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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discovery. Bayesian approaches provide a principled framework for modeling uncertainty by capturing posterior distributions over model parameters or predictions. Despite recent progress in approximate
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AI PhD Scholarship - Opportunity: Foundation Models for Brain Data Job No.: 696618 Location: Clayton campus Employment Type: Full-time Duration: The scholarship may be held for up to 3.5 years
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Professor and Head of Department - Physiology Job No.: 694423 Location: Clayton campus Employment Type: Full-time Duration: Fixed-term appointment until April 2030 Remuneration: $233,964 pa Level E
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The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based
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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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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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of theoretical work asks whether such a system could serve as a runtime guardrail, deriving probabilistic bounds on the harm probability of a candidate agent action by reasoning over a Bayesian