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Field
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qualification (doctorate) Requirements: Completed scientific university degree in geography, (geo)informatics, landscape ecology or comparable disciplines Profound knowledge of GIS software, programming languages
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fields. Additional optional skills and qualifications: Knowledge of coastal geology. Knowledge and experience in programming with Python and in GIS. Availability and motivation to participate in field
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programming and analytical skills (e.g., Python, R, GIS, modeling frameworks). Experience working with EO data, disease surveillance data, or socio-environmental modeling. Demonstrated interest in
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include strong skills in GIS, large dataset handling, Python programming, and a record of scientific publications in the field. Excellent command of English, both spoken and written, is expected. Merits
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 3 months ago
-acquired above-the-canopy mapping within the broader ForestSphere Digital Twin architecture (e.g., integration with understory maps at ground level and sensor-derived mapping data with GIS systems
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motivated individual with capacity to work independently. Good time management and planning; Good publication track record; Some knowledge of Building Energy Modelling, GIS and programming language (Python, R
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(GIS); - Programming skills in Python and MATLAB. Additional optional skills and qualifications: - Good command of English. Contracting requirements: Presentation of the academic qualifications and/or
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and maintain Python and R codebase for transit routing and GIS operation automation. · Generate and write grant proposals with Dr. Liu on various topics. · Work with undergraduate and graduate students
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. Strong programming and analytical skills (e.g., Python, R, GIS, modeling frameworks). Experience working with EO data, disease surveillance data, or socio-environmental modeling. Demonstrated interest in
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) data analytics, wrangling, management, and synthesis, (b) numerical and analytical modeling environments (e.g. Python), (c) geoprocessing and GIS analytics, and (d) data-driven model development