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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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expertise in forest ecology, disturbance ecology, and landscape ecology, and methodological expertise in harmonizing distinct databases (e.g., forest inventory, remote sensing, land cover), GIS, and R-based
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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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in data analytics and statistical methods, particularly using tools such as R, Python, or other relevant software. Experience with Data Visualization & Programming: Expertise in data visualization
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remote sensing/climate data. You have programming skills in Python and/or R; you are familiar with reproducible coding and automated geospatial data analysis. You have excellent scientific writing and
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. You have good programming skills in Python and/or R; you are familiar with reproducible coding and automated (geospatial) data analysis. You have excellent scientific writing and communication skills in
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geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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technologies and methodologies, and Geographic Information Systems software (e.g., ArcGIS, QGIS), Python, and Matlab is required. Knowledge of other programming languages (e.g., C, C++, R, Javascript) is
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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