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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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Processing/Control Path Planning/Trajectory Planning Multi-Target Tracking/Multi-Object Tracking, Bayesian Filtering, Radom Finite Set filters or closely related multi-target tracking approaches in radar
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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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with causal inference techniques such as causal graphical models, instrumental variable analysis, and counterfactual reasoning to better handle high-dimensional, multi-environment datasets typical in
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look for you, a motivated and talented individual eager to gain experience in the sustainability industry in a defined timeframe. This internship allows you to sharpen your skillset and build a track
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Australian Research Council (ARC) Funded PhD Opportunity at Faculty of Engineering: High-Speed Rail and Sustainable City Sizes in Australia Location: Clayton campus Department/Unit: Monash Institute
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The successful candidate will require advanced ability in German, and a proven track record in research fieldwork and historical methodology. In its assessment, the selection committee will prioritise applicants
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methods for elemental tracking of Ca under biotic and abiotic environments in a range of substrates and microbial metabolic pathways using stable isotopes. A 3 year PhD project available at Curtin
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-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs