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). The reason for this is that the candidate will need to be trained in theories about humans and experimental methods. Meet H1E requirements for Monash FIT PhD entry.
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Bayesian deep learning (e.g., Monte Carlo dropout, deep ensembles, Laplace approximations, and variational inference), several challenges remain: Scalability: Many Bayesian inference methods
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What makes a machine conscious? This PhD would be at the intersection of Philosophy, AI and neuroscience. You would study the latest neuroscience based theories about how consciousness emerges in
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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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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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, yielding negligible performance gains or even inducing catastrophic forgetting. To bridge the gap between theoretical AL and real-world deployment, this PhD project will develop resilient active learning
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or nats, balances model complexity against goodness-of-fit. It is essentially a formal implementation of Occam's razor. A key advantage of MML is that the message length provides a universal gauge for
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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, Melbourne. We are seeking PhD candidates interested in developing methods to assist the formative assessment and improvement of collocated teamwork, by making multimodal activity traces visible and available
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of the following would be useful: stakeholder engagement, (IT) system design, basic web programming, and technology evaluation. Experience in Indigenous and qualitative research methods is desirable, and Indigenous