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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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models Uncertainty estimation and calibration Failure and out-of-distribution detection Adaptive and selective computation Model routing and dynamic inference Generalisation to unseen environments Embodied
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Duration: 3.5-year PhD scholarship, subject to Monash University scholarship conditions Remuneration: The successful applicant will receive a Research Living Allowance, at current value of $ 37,145 AUD per
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candidates for PhD study in the Department of Data Science and Artificial Intelligence at the Faculty of IT, Monash University. As part of this scholarship, the successful candidate will develop novel
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-scale AI model training, resource orchestration, cloud/edge computing, high-performance computing, or energy-efficient computing. Monash University strongly advocates diversity, equality, fairness and
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at Monash University. The successful candidate will join our world-leading team in Temporal Analytics Lab, a world leading research group uniquely combining research in time series forecasting, classification
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then apply today! About Monash University At Monash , work feels different. There’s a sense of belonging, from contributing to something ground breaking – a place where great things happen. We value
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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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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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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