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datasets and an evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting). You
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datasets and an evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting). You
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preferably experience with ecosystem modelling, ecological forecasting, phytoplankton ecophysiology and/or trait-based ecology. Please note that we do not expect that candidates master all techniques from
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for the education in subjects of Meteorology and Air Quality at the BSc and MSc level and the training of PhD candidates. Students are guided in the basics of Weather and Climate, Forecasting, Turbulence and
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Affinity and preferably experience with ecosystem modelling, ecological forecasting, phytoplankton ecophysiology and/or trait-based ecology. Please note that we do not expect that candidates master all
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the flood scenarios to 1) early warning: a rapid operational damage assessment using flood and weather forecasts to support emergency warnings by insurers and 2) Future risk assessments by long‑term
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on portfolios; Building AI methods to integrate the flood scenarios to 1) early warning: a rapid operational damage assessment using flood and weather forecasts to support emergency warnings by insurers and 2
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to efforts in biodiversity monitoring, ecological forecasting, and conservation planning. This project is highly quantitative. During the PhD, you will develop skills in population ecology, demographic
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Air Quality at the BSc and MSc level and the training of PhD candidates. Students are guided in the basics of Weather and Climate, Forecasting, Turbulence and Dispersion, Boundary-Layer Meteorology and