300 distributed-algorithm-"Newcastle-University"-"Newcastle-University" positions in Australia
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challenging problem due to the limitations of classical algorithms. These methods often struggle with the complexity and scale of accurately predicting mRNA secondary structures. This pilot research project
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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference
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are undertaking research in aerospace engineering and specifically on the application of distributed propulsion systems on future aeroplanes. Project details: Despite being some of the most complex and efficient
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(Academic Level A) Based at our St Lucia Campus with fieldwork primarily in Palau About This Opportunity We invite applications from motivated Field Ecologists with expertise in mapping coral distributions
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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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. #sustainability Project description For distributed renewable micro-grids to become a mature technology, the economics and the reliability have to be equal to or better than today’s distribution grids. The PhD
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Status: Closed Applications open: 1/07/2024 Applications close: 18/08/2024 View printable version [.pdf] About this scholarship Description/Applicant information Project Overview Algorithmic systems
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opportunities for advancements in the entire hydrogen value chain, including production, transport, and distribution. The transportation of hydrogen to consumers poses a significant challenge, and the current gas
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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions