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will apply nonlinear and associational (colloquially called “causal”) timeseries analysis techniques to provide a more rigorous, and more statistically significant framework for understanding
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, developing spatial statistical models, and translating results into actionable insights for policy and adaptation. The strength of the project lies in its interdisciplinarity, combining atmospheric science
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opportunity to devise an exciting research project, to receive training in data capture and manipulation, statistics, trait analysis, and modelling of interaction webs, and to undertake fieldwork
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and collaborative partnerships. They will receive interdisciplinary training across microbiology, statistics, as well as working with policy stakeholders to translate research into real-world