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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data
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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal
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modeling, multilevel (random effects) modeling, and analysis of data from complex samples Experience with management and analysis of big data Experience with machine learning and related approaches (e.g
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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 About 12 billion
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are predominately held in English. Full-time / part-time part-time (study alongside work) full-time Programme duration 8 semesters Beginning Only for doctoral programmes: any time Additional information on beginning
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Generation Sequencing-based genomics and pan-genomics data, large field trial data across multiple geographical sites, and environment data. The PhD research will involve cutting-edge bioinformatics and data