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Field
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, better adapted individuals can be selected at the seedling stage using only genetic data, accelerating the breeding cycle. Incorporating information about plasticity can aid genomic prediction modeling
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evaluation of the framework in simulated/real total defense relevant environments. Duties A doctoral student position involves both theoretical and practical work. As a doctoral student, you are trained in
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applied statistical models, and will be part of a growing conservation technology hub at the department. The Department of Wildlife, Fish, and Environmental Studies offers a creative, stimulating, and
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/southern Sweden and potentially other locations (> 8 hours drive from Umeå). Other meriting qualifications are: Strong quantitative skills, with experience in statistical modeling or spatial data analysis in
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Earth-system models. Qualifications: The successful candidate must meet the following qualifications: MSc degree in Environmental Sciences, Ecology, Biology, Soil science or any other related field A
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well as high motivation to pursue doctoral studies. Previous experience with plant nitrogen physiology, trees as a model system, genetics, genomics, and bioinformatics are valuable merits. Place of work: Umeå
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fertilization, both experimentally and with modelling. You will analyze long-term growth trends using tree cores from field sites along a climatic gradient, and study physiological and morphological acclimation
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fuel structure, processing of different remote sensing and mapped data, statistical modelling, mapping of fire risk and analyzing the effect of forest management for fire behavior. Considered remote
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, bioinformatics, and ecological modeling. Results will inform future biodiversity monitoring frameworks, adaptive forest management, and conservation policy. At the Department of Wildlife, Fish and Environmental
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in this project. As successful candidate, you will investigate predisposition of trees to drought stress by long-term fertilization, both experimentally and with modelling. You will analyze long-term