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Field
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technologies (e.g., Python, R, GIS, databases) Relevant experience in data collection, data analysis, and research data management Understanding of the national and international scientific environment and
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a working knowledge of several languages such as PostGres, MS Excel, JavaScript, Python, C# and R. It is important for the candidate to be adept with QGIS and PostGIS. The ideal candidate should be
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entails the development of heuristic and metaheuristic algorithms, as well as GIS map integration, for the most effective management and suppression of wildfires. You will work in an international team
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: Python, R, SQL, and ArcGIS; • Excellent knowledge of data science techniques and experience working with the following data formats: tabular, multi-level JSON, GIS, and images; • Excellent written
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modern), historical geography, GIS, programming (esp. Python) and computer science, statistics and data science, text mining, natural language processing, demography, economics, environmental history, and
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, forklifts), and minor equipment repairs. Laboratory activities will include, but not be limited to, data systems administration, geospatial analysis (GIS and/or remote sensing), and processing of field data
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, particularly using MODFLOW. Demonstrated experience with scenario development and model-based analysis of environmental systems. Proficiency in programming or scripting languages (e.g., R, and Python
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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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GIS course focused on emerging technologies and analytical techniques in geospatial science. This course will incorporate cutting-edge tools such as Python for GIS, machine learning for remote sensing
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written