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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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are: Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context Detect healthcare-associated transmission of AMR in real-time and transform outbreak
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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in Machine Translation to produce more accurate and correct translations has a long history. However, this crucial aspect of the translation process has been largely ignored in the research community
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guidance system, informed via MR of information necessary to complete the task, but also able to supervise any machine-learning or decision-making processes. This is an ambitious goal involving many sub
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techniques. It may also include hardware development of wearable assistive devices that use audio and haptic feedback. Required knowledge Image processing Computer vision Deep learning Programming (Python, C
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for Earth" grant by Microsoft, one of only 6 projects in Australia to receive this recognition. The new project will build original frameworks for future applications of Machine Learning and Computer Vision
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Connected Autonomous Vehicle (MCAV) team. Required knowledge Artificial Intelligence Machine learning Software Testing Genetic Algorithms
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approximation algorithms for deriving dual bound within a branch-and-bound algorithms. Other directions could use Machine Learning or new decompositions. This subject is generally quite open so it is important to
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world, and with world-class photonic facilities at Monash. "Quantum nanophotonic chip" "Multimode imaging through ultrathin meta-optics" "Advancing optical imaging with flat optics" "Machine-learning