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- Swedish University of Agricultural Sciences
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public events. Independent work as well as collaboration with other members of the project team. Content of the research activity: Spatial analysis in GIS (e.g., in QGIS or Python) aimed at analysing
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research papers; present research-in-progress at e.g. workshops/conferences; contribute to spatial analysis and GIS-related courses of the department; follow a 30EC training programme to prepare for your
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activity: Spatial analysis in GIS (e.g., in QGIS or Python) aimed at analysing spatial relationships between data represented as vectors and rasters. WE REQUEST Professional education, qualifications and
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or habitats knowledge of data analysis, statistical modelling or remote sensing experience with GIS, programming (R/Python) or handling large datasets demonstrated interest in method development or biodiversity
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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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, 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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on spatial reasoning, semantic technologies and AI for geosciences. Where to apply Website https://www.academictransfer.com/en/jobs/356999/phd-semantic-modelling-of-geoda… Requirements Specific Requirements A
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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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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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changes to hydrometeorological forcing. Geomorphology, 414, 108383, https://doi.org/10.1016/j.geomorph.2022.108383 Eelsalu, M., Viigand, K., Soomere, T., Parnell, K., 2024a. Systematic analysis