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- Proficiency in R and/or Python programming - An interest in interdisciplinary research spanning ecology, remote sensing, spatial analysis and mathematical ecology - Experience in writing and publishing peer
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such as R or Python. The ideal candidate will be comfortable working with large geospatial datasets and interested in collaborating as part of a team on various CanN2ONet modelling initiatives. Experience
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, including familiarity with programming environments such as R or Python. Experience with geospatial analysis and/or systems modeling around agriculture/food/energy applications, as well as experience working
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