153 linked-data "https:" "https:" "https:" "https:" "https:" "OSU" PhD positions in Australia
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datasets to train effectively. In practice, acquiring high-quality spectral data from wet-lab experiments is expensive and time-consuming. Furthermore, relying on a single spectral modality often
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datasets to train effectively. In practice, acquiring high-quality spectral data from wet-lab experiments is expensive and time-consuming. Furthermore, relying on a single spectral modality often
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Skip to content Next Generation Graduates Program - Sports Data Science Scholarships (Honours, Masters and PhD) Future student scholarship Scholarship details Study levels Student type Future
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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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well as to allow video information to be shared for both marketing, analytics and editorial purposes. By accepting optional cookies, you consent to the processing of your personal data - including
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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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from an unbiased strategy space.” Journal of the Royal Society Interface 16 (2019): 20190127. https://doi.org/10.1098/rsif.2019.0127 Perera, I., de Nijs, F. and García, J. “Learning to cooperate against
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from an unbiased strategy space.” Journal of the Royal Society Interface 16 (2019): 20190127. https://doi.org/10.1098/rsif.2019.0127 Perera, I., de Nijs, F. and García, J. “Learning to cooperate against
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loading strategies for managing highly-irregular flows of data from many sources. Develop approaches to model the performance of the aforementioned event-triggered systems, and to use these models to