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Field
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning
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recognized leader in cartographic design, dynamic mapping, mobile application development, spatial data analysis, visualization, and GIS. The Lab conducts interdisciplinary collaborative projects with research
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statistical software (R, Python, SAS, or similar); English proficiency for reading, writing, and presenting; full-time residence in Piracicaba. Desirable requirements Experience with large datasets and metadata
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expertise in ecosystem service valuation frameworks (e.g., InVEST, natural capital accounting, non-market valuation). ○ Advanced spatial analysis capabilities in GIS (ArcGIS, QGIS) or spatial programming in R
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Previous Job Job Title Post-Doctoral Associate: Khoruts Lab, GI Division Next Job Apply for Job Job ID 375581 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary
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» BiodiversityEducation LevelPhD or equivalent Skills/Qualifications Proficiency in the Python programming language, inferential statistics and mapping (GIS). Specific Requirements PhD in marine ecology with skills in data
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Qualifications PhD in Mathematical Economics and Social Science Candidates should have experience in at least one of the following areas: Statistical programming and data analysis using R, Python, or Matlab
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Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Strong proficiency in Python and geospatial data-processing tools (GIS, raster/DEM and satellite imagery); knowledge
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of a geographic information system (GIS) for estimating residential exposures and producing geographic databases. 2/ Epidemiological analyses and validation of methods • Develop and apply spatialized
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analytical skills and experience with spatial modelling and statistical software such as R, Python, or MATLAB. Experience using GIS, side-scan sonar, multibeam bathymetry, underwater imagery, remote sensing