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                Field
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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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                and Stata. Working with large datasets, including merging files into the data archive. Creating clear data visualizations to communicate results (including GIS maps). Assisting with the preparation 
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                escrita en español e inglés - Experience in analysing traffic accident data and road safety - Advanced knowledge of spatial analysis and Geographic Information Systems (GIS). - Proficiency in statistical 
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                analysis: Applying geospatial methods (GIS mapping, geographically weighted regression, spatial clustering) and temporal approaches (time-series analysis, distributed lag models, case-crossover designs 
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                for NCDs. This will involve: Spatial analysis: Mapping and modelling environmental exposures at fine spatial resolution using GIS tools, geographically weighted regression, and spatial clustering techniques 
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                monitoring. Responsibilities Drive forward the department’s analytical and statistical expertise in remote sensing, LiDAR, AI tools, GIS, and spatial modeling. Take a leading role in developing digital tools 
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                monitoring. Responsibilities Drive forward the department’s analytical and statistical expertise in remote sensing, LiDAR, AI tools, GIS, and spatial modeling. Take a leading role in developing digital tools 
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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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                on spatial remote sensing and geographic information systems (GIS). The new mathematical models developed as part of the Math-Vive PEPR will serve as a basis for building predictive analysis models and 
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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