246 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"L2CM" positions at Monash University
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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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their uncertainty to different stakeholders, and evaluate the effect of the conveyed information. The expected outcome of this project is an innovative conversational agent that helps users (e.g
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also provides additional accommodation support to students through a Preferred Accommodation Provider Program and the delivery of information and resources on the private rental market for the broader
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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projects that involve data analysis, the application of artificial intelligence, the development of new detection techniques, and the exploration of new experimental methods through collaboration with our
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major research initiative developing a long-term data asset to improve care for people with moderate to severe traumatic brain injury. Based within the Pre-hospital, Emergency and Trauma Research (PET
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traditional and advanced optimization techniques, including analytical models, simulation-based approaches, and data-driven algorithms. The research also considers practical constraints such as cost, process
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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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background in AI/ML, data science, or signal processing Interest in music informatics, emotion modelling, or multimodal AI Ability to implement and evaluate machine learning models independently Commitment
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, to trace the chemical enrichment of the universe, and even to better understand planet formation. Most of my research involves huge data sets with observations of all different kinds (e.g., photometry