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Field
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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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., 1st March 2026). Specific Requirements Solid practical experience with GIS (e.g., QGIS, ArcGIS), including spatial data processing and visualization; Experience with data analysis using Python, Matlab
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may include site visits and data collection. Experience in numerical modelling, GIS, or hydrodynamics is desirable, but not essential and training will be provided. Prior research experience and
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techniques such as thermal drones and AI modeling, with Python, R-Studio, Yolo, SLEAP, LabGym. The applicant must have proficiency in RStudio and GIS tools, as these skills are essential for data analysis and
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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: The doctoral degree must have been obtained at least 1 year ago; Proven experience in GIS environment analysis (QGIS, ArcGIS, R), statistical analysis and data processing in R or Python - information provided in
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Economics, Computational Science, Geography, Environmental Studies, or Engineering & Policy Analysis; Knowledge of a programming language (Python, Julia, etc) and training in any of the simulation methods
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processes causing consecutive landslides will be undertaken. Training The individual joins a team of international experts who will support through training in remote sensing and GIS, field geomorphic
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, Environmental Studies, or Engineering & Policy Analysis; Knowledge of a programming language (Python, Julia, etc) and training in any of the simulation methods; Experience with (statistical) data analysis