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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Switzerland | about 1 month ago
understanding of physical and chemical processes in soils. You have experience with geospatial modeling and are proficient in a programming language relevant to data analysis (ideally R). Additionally, you
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would have strong experience in some of the following areas: quantitative and qualitative data collection and analysis, geospatial data analysis, urban design, data visualization, and community engagement
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data sets and climate model outputs Analytical skills, including experience with multivariate analysis, time-series, and geospatial statistics Previous research experience and publication record Ability
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, experience in a relevant aspect of health geography and geospatial science, along with excellent communication skills and experience in collaborating with researchers from different disciplines. A mix of
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, crop yield). -Familiarity with geospatial data and tools (e.g., GIS, QGIS, Google Earth Engine). -Knowledge of explainable AI (e.g., SHAP, LIME), model interpretation, and/or uncertainty quantification
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that contributes to more just, equitable, and livable cities. The ideal candidate would have strong experience in some of the following areas: quantitative and qualitative data collection and analysis, geospatial
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an emphasis on geospatial data analysis. The ideal candidate should have strong command of computational methods and experience working with large scale, high resolution data such as satellite data, street
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skills in Python or R for geospatial analysis. Strong quantitative, communication, and scientific writing skills (papers, talks). Application Requirements Document requirements Curriculum Vitae - Your most
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environments (MPI, batch schedulers). Hands-on experience with geospatial data processing (NetCDF/HDF5, GDAL, xarray) and coastal datasets (e.g., GEBCO/ETOPO, USGS/NOAA/NCEI), including mesh generation and
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carbon flow through different pools. Projections will use field data as well as available geospatial climate, land use, and soil data. You will do this in close collaboration with project partners from