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to interdisciplinary discussions Office, GIS (Arc/Q), R, Python (basics) English, Spanish (Italian, German, Dutch, Polish beneficial) Strong experience with spatial assessments. Early leadership experiences at junior
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. • Strong proficiency in Python and relevant libraries for data analysis and modelling (e.g., TensorFlow, Keras, Scikit-learn, Pandas); knowledge of R would be an asset. • Familiarity with geospatial data
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Andres Masegosa (arma@cs.aau.dk), Department of Computer Science. (please see: https://andresmasegosa.github.io/ . The project’s domain PI is Professor Jamal Jokar Arsanjani (jja@plan.aau.dk), Department
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. Proficiency in at least one of the GIS tools: ArcGIS or QGIS. Proficiency in at least one of the following programming languages: R or Python. Other Requirements or Other Factors: May travel to sites for field
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record in peer-reviewed international journals Experience with remote sensing, LiDAR, and GIS applications Programming skills in Python Background in LiDAR point-cloud analysis and vegetation structure
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, dynamic mapping, mobile application development, spatial data analysis, visualization, and GIS. The Lab conducts interdisciplinary collaborative projects with research partners on campus at the UO, with
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software (e.g. ArcGIS, QGIS) and coding environments (e.g. Python or R), collaborating across LUMHR themes, and supporting interdisciplinary research activity. Teaching support may be required, up to a
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undergraduate or graduate researchers. · Experience writing research grant applications. · Experience with software such as R, Python, Matlab, and GIS tools · Experience facilitating workshops
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Information Science (GIS), and computational science for health and environment, to study processes spanning from the microscopic to the planetary, across all time scales. The Inverse Modelling group at the Department
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well as numerous other benefit programs. https://www.tamus.edu/business/benefits-administration/ What you need to know The anticipated start date for this role is June 1, 2026, pending final hiring processes. TTI is