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computation is necessary before taking an action. This PhD project will address these challenges by developing trustworthy and resource-adaptive VLA models. A central question is whether an embodied agent can
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PhD Scholarship - Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure Job No.: 696617 Location: Clayton campus Employment Type: Full-time
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Focus The PhD candidate will undertake research including: Experimental characterisation of aerosol generation, plume structure, droplet-size distribution, velocity fields and entrainment in a jet
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PhD Scholarship in Multimodal Federated Learning and Medical Image Analysis Job No.: 695949 Location: Clayton campus Employment Type: Full-time Duration: 3-year and 3-month fixed-term appointment
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hospital or population often fail when applied elsewhere due to distributional shifts. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and
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AI infrastructure. The Opportunity This is an unprecedented opportunity for an outstanding data science and AI PhD candidate interested in brain data analysis and AI, supervised by Dr Mahsa Salehi
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paradigms rely on a fragile "closed-world" assumption: that the unlabeled pool perfectly reflects the distribution of the labelled seed set. In real-world deployments, this is rarely true. Data streams
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, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure
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Evidence synthesis is only as strong as the methods behind it, and the people willing to push those methods forward. This Associate Professor (Research) role sits within the Australian Living Evidence
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missing modalities and distribution shift. Design uncertainty-aware decision frameworks for downstream tasks. Expected Contributions This PhD project is expected to contribute: Scalable Bayesian uncertainty