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Field
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, the project will enable the evaluation and optimization of resilience strate-gies. The framework will be validated through pilot studies, ensuring its applicability to real-world industrial chal-lenges
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of econometric analysis and experience using Stata and/or R. Experience with administrative databases will be valued. Skills in map creation and Geographic Information Systems (GIS) will be valued. Experience in
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desired. Knowledge on statistical methods and their application is an extra merit. Good knowledge in GIS and R is a merit. Proven excellence in written and spoken English is essential. The fieldwork will
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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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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