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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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with Microsoft Office Suite and familiarity with statistical and geospatial software such as Python, R, and GIS applications (e.g., ArcGIS, QGIS). Ability to work collaboratively as part of a
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, and statistical analysis. Proficiency in data analytics, visualization. Proficiency in at least one of the following programming languages: R or Python. Proficiency in Microsoft Office (including
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; - No employment contract, or proof of full unpaid leave, in accordance with FAPESP regulations . Desired Skills: - Proficiency in R, Python, and JavaScript (Google Earth Engine); - Geoprocessing and GIS
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. Excellent proficiency in spatial and statistical programming (e.g., R, Python, or Google Earth Engine). Proficiency in, and commitment to, version control (e.g., Git), reproducible research practices, and
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predictive modelling Public policy evaluation Management and analysis of survey data Software proficiency Statistical and econometric packages such as Stata, R, or Python GIS software (QGIS, ArcGIS
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, Natural Resources, Sustainability, or closely related field. Demonstrated expertise in spatial analysis & GIS and remote sensing for land/water/agricultural applications. Strong programming skills in Python
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REDCap, Qualtrics, or similar platforms. Proficiency with at least one programming language for data analysis (e.g., Python, SAS, R). Experience working with healthcare or secondary datasets, including
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Geography, Environmental Science, Earth Systems Science, Natural Resources, Sustainability, or closely related field. Demonstrated expertise in spatial analysis & GIS and remote sensing for land/water
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, Natural Resources, Sustainability, or closely related field. Demonstrated expertise in spatial analysis & GIS and remote sensing for land/water/agricultural applications. Strong programming skills in Python