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: Comparative transcriptomics, orthology inference, positive selection detection, protein domain analysis, phylogenetic comparative methods Computational skills: UNIX/Linux, HPC computing, R, Python You will gain
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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hearing science, or a related discipline, and a keen interest in developmental research. Prior experience with experimental design, data analysis (preferably in R), eye-tracking, or developmental
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genomes from museum collections, to track changes in genetic diversity, inbreeding and genetic load directly over time, and how the influx of new genetic material has shaped these parameters. The project
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perception of object shape and its encoding in languages across the world. The successful candidate will work with Prof. Larissa Samuelson on a longitudinal study tracking how children from 24- to 42-months
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) enhance the utility of genetic markers for tracking Pst resistance genes in breeding programmes. With the overarching goal to support diversification of resistance sources within the UK breeding pipeline
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the pathways and fate of the Atlantic Water that enters the Arctic Ocean through virtual particle tracking and heat budget analysis. Study how sea ice losses influence ocean-atmosphere-ice exchanges