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Field
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experience with analytical software like Stata, R, or Python, are essential. The ideal candidate will also have experience with geospatial tools. Additionally, you will have experience designing and managing
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environmental planning, or land development economics, and statutory planning. Proficiency in Geographic Information Systems (GIS), including spatial data analysis, mapping, and geospatial visualisation
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, remote sensing data analysis, images processing, and geospatial analysis. Experience working with satellite imageries (e.g., Sentinel-1, Landsat, Sentinel-2) and other remote sensing data sources
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requires an academic degree in the discipline (Geography, Geospatial Technologies, Planning, Ecology, Environmental Studies, or Sustainability). Candidates for Lecturer position will have at least 18
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learning development environments e.g., PyTorch, TensorFlow. · Knowledge of Geospatial applications of Machine Learning. · Familiarity with current software development best practices, e.g
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anonymized data sets through the assignment of unique identifiers Standardizes data formats and ensures data accuracy and consistency. Manages geospatial data, including Shapefile editing and geocoding Creates
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and water dynamics, and plant breeding Working knowledge of crop-climate modeling e.g., DSSAT Advanced remote sensing and geospatial data analytical capabilities Experience implementing, training and
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skills in working with databases and SDE/SDF connections used in geospatial environments. Experience in working with space management or asset tracking applications and integrations is an asset. Experience
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-based analysis, geospatial analysis, predictive analysis, data visualization, data cleaning, and data communication for a wide range of audiences. Modes of Work Positions that are eligible for hybrid
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geospatial modelling of agricultural management practices across Canada, particularly those related to fertilizer use and cropping practices. As part of this, the postdoctoral researcher will contribute