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Python, and used to working with large datasets and reproducible analysis workflows. Has a demonstrated ability to initiate and drive own research ideas, preferably with experience from writing and
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(or equivalent) in Computer Science, Statistics, Ecology, Biology or Forestry. · Documented experience with application of deep learning and advanced statistical analysis and programming (e.g., R or Python
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R and/or python. Familiarity with biodiversity assessments, aquatic ecology, boreal forest ecology, and forest management. Ability to work both independently and in collaborative teams. Field work
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analysis, statistical modelling or remote sensing experience with GIS, programming (R/Python) or handling large datasets demonstrated interest in method development or biodiversity research Great emphasis
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management Nordic forestry Remote sensing data: ALS, TLS, satellite (e.g. sentinel2), aerial images Statistical modelling and analysis GIS e.g. ArcGis, Qgis, R Programming, e.g. R, Python, C etc. Field work
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methods, or forest ecology - Advanced Skills in R/Python, GIS, bioinformatics, and molecular lab work - Ability to work independently and in multidisciplinary teams - Strong English communication skills