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on representation stability, interpretability, and efficiency.Despite their success, self-supervised approaches and foundation models still lack a thorough theoretical understanding. This project aims to bridge
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. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in methods for causal inference in observational data, is strongly preferred. Using various
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to investigate ecological interactions. • Statistical models: Use GLMMs and null models to assess environmental and temporal effects on microbial and dietary communities. • Identify key species
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