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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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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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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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-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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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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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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distribution functions. Total content of H2 in the solar neighbourhood and nearby galaxies and how they compare to other gas/dust tracers. Extinction mapping from resolved stars in nearby galaxies. Studying mass
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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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