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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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related field Demonstrated experience in geospatial analysis (GIS) and proven skills in hydrogeological or hydrological modeling Proficiency in programming (e.g., Python) Ability to handle large datasets
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and AI algorithms Solid programming skills in Python and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience working with geospatial data (e.g., geopandas
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with handling and harmonizing large and variable datasets, statistical analysis, species/ habitat distribution modelling, use of R/Python, GIS, preferably open source GIS (e.g. QGIS, GRASS). Essential is
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of coastal and marine monitoring data Experience in numerical model data extraction with Python and data analysis in R as well as experience in spatial data analysis with geo-information systems (GIS
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well as European coastal and marine policies (especially EU-MSFD, EU-WFD and EU Nature Restoration Law) Experience in numerical model data extraction with Python and data analysis in R as well as experience in
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production, agriculture broadly, and/or smart technologies is desirable. • Experience in modelling biological or agricultural systems, with strong programming skills (R, Python, or Matlab