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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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genetic, phenotypic, and environmental data, testing when and how evolution can be forecast. As a PhD student in our group, you will gain hands-on experience in computational and mathematical modeling and
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research group within the Division of Water Resources Engineering focuses on integrating various satellite remote sensing and AI technologies for monitoring, modeling and forecasting hydrological elements
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attributes are shifting in the presence of probabilistic outputs, non-determinism, and continuous data dependencies, and how these factors impact maintainability, testability, and reusability. The PhD project
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for monitoring, modeling and forecasting hydrological elements such as precipitation, evapotranspiration, floods, wildfire, etc. The present PhD project will focus on developing an AI-based decision support system
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. The work is performed in connection to two ongoing research projects “Probabilistic multiscale modelling of the macroscopic crack growth behavior in heterogeneous materials” and “Optimized Digitalization