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experience with knowledge graph standards (e.g., RDF, OWL, SHACL); familiarity with GIS, geodata infrastructures and geo-analytical workflows some experience with AI and machine learning methods to label texts
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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· Data handling, processing and modelling · Familiarity with GIS tools (e.g., ArcGIS, QGIS is a plus · Proficiency in Python scripting for data analysis and automation is a plus
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characterization of deep-water habitats, GIS spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and
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Professor that will be capable of contributing to multiple ongoing research projects in the lab. Potential projects include, but are not limited to, oceanographic characterization of deep-water habitats, GIS
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topographic indices as environmental variables for mapping, and satellite data for weather. The doctoral student will be part of a broad research group with expertise in GIS, AI, soil science, forest ecology
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for policy, practice and advocacy. The mixed-methods project will use a combination of participatory approaches including but not limited to GIS mapping, stakeholder analysis, network and systems mapping
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independently and collaboratively Excellent written and verbal communication skills Desirable Experience with modelling tools (e.g., MATSim, UrbanSim, ActivitySim, GIS, or Statistics) Experience in stakeholder
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Information Systems (GIS), programming in Python, practical forestry, nature conservation, cultural heritage management, as well as a driver’s license, are considered assets. Place of work: Umeå Forms
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, institutional analysis, and resource-use conflicts. The Departments of Human Geography, Physical Geography and Cartography, GIS and Remote Sensing are closely linked with the Centre for Biodiversity and