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- Swedish University of Agricultural Sciences
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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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                modelling and statistical and GIS software (R, QGIS/ArcGIS, Python). - Excellent scientific writing and communication skills in English and Spanish. Specific Requirements • Doctorado en Programas de 
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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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                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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                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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                Wetsus - European centre of excellence for sustainable water technology | Netherlands | about 2 months agodata, GIS, and environmental modeling. Familiarity with programming languages such as Python, Julia, R, C++, or MATLAB is considered a strong asset. Experience with fieldwork or working with soil 
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                ) Documented record of advanced quantitative methods skills in R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches 
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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