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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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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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) scientific studies Experience with relevant field data collection methods (e.g. chamber- or eddy covariance-based C flux measurements, biodiversity sampling methods) Computer programming skills (e.g. Matlab, R
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experience in molecular biology and plants as an experimental system, and knowledge of statistics in, for example, the programming language R are required. It is important that the applicant has the ability
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driver’s license for fieldwork. Beneficial qualifications are Experience in tree physiological measurements. Experience in fieldwork. Experience in programming / modeling in R and/or other software
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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
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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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and development. Drivers license is a necessity for the work duties. Beneficial qualifications are experience of forestry analytics, using preferably R programming language and/or GIS softwares, and
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languages such as R or Python, working with data in a Linux environment. Great emphasis will be placed on personal qualities such as commitment, ability to collaborate, analytical skills, independence, and