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familiarity with health-related datasets. Additional Qualifications: Proficiency in statistical software (R, Python, etc.), and working knowledge of data management protocols. Experience with exposome
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mapping, GIS and drone photogrammetry for 3D results of specific case studies. This role directly contributes to the development of a high-resolution comparative spatial atlas of long-term refugee camps by
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into operations. The position includes learning AWIPS (Advanced Weather Interactive Processing System), advancing Python skills to assess product quality, helping develop training materials, and presenting
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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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accepted for publication. Expected skills Proficiency in programming (R, Fortran, Python) and the ability to learn new programming languages, develop code, and analyze data. Knowledge of GIS tools and
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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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., 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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SWAT+gwflow Proven experience in modelling natural and human-induced drought (streamflow and groundwater drought). Proficiency in programming (R, Python, or MATLAB) and GIS tools. Experience in climate
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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