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without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research/learning
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question will be developed with the student and could focus on publishing, peer review, research funding, scientific careers, collaboration, or the norms governing AI use. Projects may also use data from
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simulation techniques, as well as available data from scientific publications, citations, and science career trajectories. The goal is to design better incentives for scientists to produce their best work
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simulation techniques, as well as available data from scientific publications, citations, and science career trajectories. The goal is to design better incentives for scientists to produce their best work
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Primary supervisor Chern Hong Lim Co-supervisors Bisan Alsalibi Yasmeen George Research area Data Science and Artificial Intelligence While deep learning has shown remarkable performance in medical
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
Primary supervisor Sicily Fung Fung Ting Co-supervisors KokSheik Wong Dr Patrick Tan Hock Siew Prof. Qasim Ayub Dr. Sara Subhan Dr. Aswini L. Loganathan Research area Data Science and Artificial
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Primary supervisor David Dowe Co-supervisors Nenad Macesic Research area Data Science and Artificial Intelligence Antimicrobial resistance (AMR) is one of the most significant and immediate threats
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programming background with fundamentals in machine learning and data science. Experience in building visualisations and interactive immersive environments (using game engines like Unity3D) is
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sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Among the approaches used will be the Bayesian information-theoretic
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sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Among the approaches used will be the Bayesian information-theoretic