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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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equivalent. Specific knowledge of hydrology, urban water engineering, basic computer programming (e.g. Matlab or Python) and experience carrying out measurements of stream flow (or similar) are meritorious
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field measurements (e.g. related to soil–water processes or geophysical approaches), and basic computer programming skills (e.g. MATLAB or Python) are meritorious. Other important skills include
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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