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are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints. Existing predictive models provide useful but incomplete support because they cannot fully
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, socioeconomic, and geospatial data; Contribute to patient-level and population-level modelling approaches, including temporal, spatial, and multimodal prediction frameworks; Apply and evaluate methods for disease
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. Existing predictive models provide useful but incomplete support because they cannot fully capture the complexity and heterogeneity of archaeological data. This underscores the need for approaches
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different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement learning, as
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state-of-the-art approaches including single-cell and spatial transcriptomics, circuit tracing and connectomics, and automated behavioral analysis. In close collaboration with the Verstreken and de Wit