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/PhD) or related field. - Simulation & data: TRNSYS (or similar), time-series processing; Python (pandas/numpy). - Experience with GIS and/or climate/solar datasets (e.g., METEONORM, PVGIS
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, Urban Studies, Urban Analytics, Environmental Science, Computer Science, Architecture, or an appropriate master’s degree. Familiarity with Python/R programming, GIS and spatial analysis (e.g., ArcGIS
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: The doctoral degree must have been obtained at least 1 year ago; Proven experience in GIS environment analysis (QGIS, ArcGIS, R), statistical analysis and data processing in R or Python - information provided in
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are looking for an enthusiastic individual with a degree in a quantitative discipline. Experience of geospatial analysis (with GIS) is essential and programming with code (e.g. R, Python) would be advantageous
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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Economics, Computational Science, Geography, Environmental Studies, or Engineering & Policy Analysis; Knowledge of a programming language (Python, Julia, etc) and training in any of the simulation methods
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, Environmental Studies, or Engineering & Policy Analysis; Knowledge of a programming language (Python, Julia, etc) and training in any of the simulation methods; Experience with (statistical) data analysis
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Additional Information Eligibility criteria Technical skills: proficiency at ecological modelling, use of the Unix/Linux environment, proficiency at oceanographic data repositories and GIS tools, good
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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