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University of the Virgin Islands | Saint Croix Falls, Wisconsin | United States | about 12 hours ago
data collection using the SigCap Application. Data processing and analysis in Python. Assess how environmental conditions and context (weather, indoor/outdoor settings) influence reception quality
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data collection using the SigCap Application. Data processing and analysis in Python. Assess how environmental conditions and context (weather, indoor/outdoor settings) influence reception quality
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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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will lead the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form
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) data sets in R or Python as well as GIS software such as QGIS or ArcGIS Pro. Knowledge of US forest ecosystems and background in analyzing forest structure, stand dynamics, biodiversity, and/or other
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., examples of program codes (Python, R, SQL, GIS scripts, etc.), description or documentation of technical solutions to research problems in which the applicant participated (spatial data processing
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, Geoscience, Soil Science, Physics, or related fields). Preferred skills: Commitment to contributing to outreach activities and research dissemination. Experience in computer programming (R+, Matlab, Python
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: Position 1: Postdoctoral Associate – Software Engineering & Technical Development Proficiency in Python, geospatial libraries, API design (e.g., FastAPI), and cloud/HPC environments. Experience with
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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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programming and strong quantitative skills. Desirable Demonstrated knowledge of advanced biogeographic, comparative, and phylogenetic methods, quantitative methods in biodiversity studies, GIS in R, and spatial