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augmented reality applications for history education and heritage visualization. Multimodal Technologies and Interaction, 3(2), 39. Endacott, J., & Brooks, S. (2013). An updated theoretical and practical
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of the art data science approaches (text mining, machine learning, AI) to comprehensively highlight yet undiscovered virus/host/environment relationships and annotate potentially putative new spillover
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internet. Using an interdisciplinary approach that includes Indology, anthropology, sound studies, media studies, art history, and the history of religions, the project will create comprehensive sonic
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visualization. Experience with GWAS, Bayesian modelling, and/or machine learning applied to biological data. Strong programming skills (R, Python) and ability to manage large-scale -omics datasets. Good
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(Working with data supplied by the project partners, Designing visual representations of these data adapted to the analysis contexts, Studying the effects of these representations on understanding