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, alongside advanced understanding of genetic resource conservation and forest ecology. Technical proficiency: Experience with GIS, databases, and genetic and statistical methodologies; familiarity with R is
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, forestry, bioinformatics, or a related field - Strong demonstrated interest in biodiversity, molecular methods, or forest ecology - Advanced Skills in R/Python, GIS, bioinformatics, and molecular lab work
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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
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students popularisation of science experience with one of the common GIS software (e.g., Qgis, ArcGIS pro) Specific Requirements Application procedure The information for the PhD admission is available
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ability to work effectively both independently and in a team environment Merits: Experience in method development, working with spatial data, and GIS Experience with univariate and multivariate analysis and
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Forest ecology and 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
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modelling, and some experience with linked data standards and knowledge graph tools (e.g., RDF, OWL, SHACL); familiarity with or interest for GIS, cartographic maps, geodata infrastructures and geo-analytical
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). • Experience handling large datasets from empirical studies, surveys and GIS desirable. • Interest and experience conducting research in livestock systems, agricultural technologies. • Interest in