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an Automated Geospatial Model-based Decision Support Tool for Assessing Road Culvert Vulnerability on US Forest Service Experimental Forests, using Geospatial Engineering & Technology skills and experience
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institutes and centers. The Data and Democracy Research Lab is a unique interdisciplinary team combining expertise in mathematics, algorithm design, geospatial data, and public policy. Members of the lab
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prior to employment. Desired Qualifications Desired Qualifications Experience in crop models such as DSSAT and APSIM. Expertise in UAV operations. Familiarity with GIS (ArcGIS/QGIS), geospatial libraries
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 17 days ago
with identifying, forming, and developing synergies and networks with stakeholders, including but not limited to policymakers, academic experts, or non-governmental organizations. Skills in geospatial
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science, computer science, or a related discipline is required by the start date. Candidates should have strong Python and geospatial-data analysis experience, demonstrated independent research ability, and
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, or agricultural greenhouse gas estimation. Proficiency in working with spatial data and geospatial tools. Interest in science–policy interfaces. What you will do Develop and apply biophysical and economic models
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for a postdoctoral fellow position. This project will focus on geospatial analysis to identify data and research monitoring gaps in the Northern Gulf of America, as a first step toward developing a
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the Geospatial Center for of the Arctic and Pacific (GCAP). Specific areas of focus will include development and investigation of geodetic tools, models, and workflows and enhancements of geodetic infrastructure
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, you will bring: Proven leadership experience within a high-growth SaaS environment. Experience with data analytics, geospatial, property development, urban design, and/or urban planning sectors. Proven
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hydro-climatic conditions govern vegetation behaviour, and how vegetation impairs the functioning of drainage and water-management assets. Using advanced geospatial modelling, machine learning and digital