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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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. Experience with trait-based ecology, ecometric or functional-trait modeling, or macroecological analyses. Experience with R, Python, or equivalent programming languages for statistical and spatial analyses
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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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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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qualifications in probilistic risk modelling, applied statistics and familiar with quantitative risk modelling measures strong data analysis skills and proficiency in R and Python; experience with other programing
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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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Bioinformatics expertise of Dr. Raimondi on the development of GI NN methods and their application to relevant biological problems with the expertise of Dr. Bry and Dr. Trottier on the statistical inference
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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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record of publications in relevant fields. Have proficiency in data processing and statistical analysis, with experience in programming languages/software such as MATLAB, Python, R, Stata, as well as GIS
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experience in remote sensing and GIS, strong programming skills (Python, R or similar), and an international publication record. Experience with machine learning and cloud computing will be considered an asset