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Field
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, Python, GitHub, fieldwork, laboratory workflows, or grant/manuscript development. Experience with R, Git/GitHub, phylogenomic or population genomic workflows, GIS/spatial analysis, herbarium curation
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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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standardization; knowledge of geospatial analysis (GIS, QGIS, Google Earth Engine); experience with carbon and soil health metrics and assessment frameworks; previous publications on soil health and soil carbon
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, for example using Python or comparable tools, and experience with GIS-based spatial analysis. The ability to work with heterogeneous geological, hydrogeological and monitoring datasets and to connect
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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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)design; integrating multi-source datasets, including street-view imagery, GIS data, smartphone mobility data, and qualitative insights; applying and adapting state-of-the-art foundational AI and machine
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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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or community-engaged interviews Data visualization and GIS skills Familiarity with high-frequency mobility/spending data (e.g., Advan, SafeGraph) or satellite-derived environmental data Anticipated Pay Range
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or community-engaged interviews Data visualization and GIS skills Familiarity with high-frequency mobility/spending data (e.g., Advan, SafeGraph) or satellite-derived environmental data Anticipated Pay Range
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, Spatial data analysis or GIS Whole-genome sequencing, phylogenetics, or phylodynamics Data management and reproducible analytical workflows Quantitative modeling and statistical inference. Applicants