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will unite ecology, statistics, and philosophy to improve the modeling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key
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multiome RNA-seq, ATAC-seq and massively parallel reporter assays (MPRAs) for unbiased genome-wide analysis for understanding the phenotypic plasticity in different cancer cell states. Work tasks The work
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the modeling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data sources, and
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philosophy to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and
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. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data sources, and account for uncertainty in ways that are relevant to real