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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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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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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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team and achieving goals collaboratively. You plan and complete tasks independently and to a high standard. You have strong analytical skills. You are proficient in scientific programming in Python
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good performance in your Master’s studies in Electrical Engineering, Computer Science, Geoinformatics, Energy Systems, or related field Solid programming skills in Python and familiarity with machine
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qualifications in probilistic risk modelling, applied statistics and familiar with quantitative risk modelling measures strong data analysis skills and proficiency in R and Python; experience with other programing
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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