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cracking resistance as compared to GI galvanized steel. Furthermore, it is unclear at this moment how these types of coatings will perform in application to the green steel. Therefore, this project is aimed
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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