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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 14 hours ago
teaching interests complement and enhance our existing departmental strengths (https://www.utm.utoronto.ca/geography/ ). Candidates must have teaching experience in a degree-granting program, including
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must have earned a Ph.D. in wildlife science, landscape ecology, geospatial science, waterfowl ecology, or a closely related field. Preferred qualifications include experience with: GPS telemetry and
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data (e.g. instrumental, laboratory, environmental, numerical and categorical data, text, geospatial data or time series); c) Experience in Python programming and in data analysis and machine learning
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human, cultural, and physical systems. Geography seeks to study and understand the distribution, dynamics, and interrelationships between humans and their physical environment. Geospatial Sciences applies
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University Classification Job Code: 8352DA The Accessibility Observatory or AO (https://access.umn.edu), a CTS research program, is a leading national resource for the research and application of access
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pipelines for the management, analysis, and visualization of geospatial data and ancillary cartography using R, Python, and Google Earth Engine. 3. Desarrollo de workflows reproducibles para la validación de
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undertake the following tasks: Process and analyse geospatial data with a view to conducting studies to assess vulnerabilities in the face of different natural hazards. Running simulations to implement and
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Graduate Certificates in Geographic Information Science and Technology; an M.S. in Human Security and Geospatial Intelligence; an M.S. in Spatial Data Science, an M.S. in Spatial Economics and Data Analysis
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The professorships “Big Geospatial Data Management” and “Information Systems” are seeking to fill a research associate position (doctoral candidate) in close collaboration with each other to support
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) Evaluate the implications of these associations for extraction processes; and 3) Assess the critical mineral resource potential of unconventional wastes through geospatial and statistical analysis of supply