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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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driven and meticulous Research Officer to support a ground-breaking program at the forefront of gastrointestinal cancer research. Based within the Gastrointestinal Translational Research Laboratory
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, representing a significant reduction of the cost of the MRI scan per patient. This project will develop cutting edge data processing methods for mobile MRI scanners and testing in clinical enviroment.
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The brain is a complex system and monitoring and imaging methods to observe critical neurophysiological variables underlying brain function are limited. This project works at the intersection
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Agent-based computational simulations are now widely employed to study the evolution of behaviour, e.g., predator-prey simulations, the evolution of cooperation and altruism, the evolution of niches
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In this project, we will use machine learning methods to diagnose the health status of bee colonies and individual bees. Bee populations are threatened worldwide due to a number of factors
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This project aims to identify novel methods for inferring actors, activities, and other elements from short message communications. Covert communications are a specialist domain for analysis in
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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
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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Minimum Message Length '', Springer (Link to the preface [and p vi , also here ]) Wallace, C.S. and D.L. Dowe (1994b), Intrinsic classification by MML - the Snob program . Proc. 7th Australian Joint Conf