Environmental Modelling (M.Sc.)
University of Oldenburg, Germany
Core: Differential/stochastic models, data assimilation, Bayesian inference, uncertainty analysis.
Geospatial: GIS/remote sensing, spatial statistics, habitat and land-use models.
Domains: Hydrology/flood risk, air quality, nutrient cycles, biodiversity.
Tooling: R/Python stacks, workflow automation, reproducibility.
Decision Support: Scenario analysis, MCDA, and policy translation.
Thesis: Build and validate a model; provide clear guidance with confidence intervals.
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