12 data-"https:" "https:" "https:" "https:" "https:" "https:" research jobs at Swedish University of Agricultural Sciences
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Swedish University of Agricultural Sciences (SLU) is seeking a postdoctoral researcher with strong methodological expertise in AI and computer vision for research focusing on animal welfare and
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place at undergraduate and advanced levels, mainly in programmes leading to degrees in agronomy and biology. For more information about the department or division visit: https://www.slu.se/en/about-slu
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for coastal ecosystems. You will perform statistical analyses of time series and spatial data on ecosystems and human activities and participate in more holistic analyses of socio-ecosystems. You will also
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ecosystems and how these should be managed today and in the future. For more information: http://www.slu.se/en/departments/forest-ecology-management/ Read more about our benefits and working at SLU by visiting
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students as well. For more information about the department or division visit: https://www.slu.se/en/about-slu/organisation/departments/department-of-plant-biology/ Read more about our benefits and working
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contributes to various courses together with other departments. For more information about the department: Department of Plant Biology Read more about our benefits and working at SLU by visiting: https
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increasing knowledge about ecosystem function. The work includes method development for AI-supported analyses of video data, application of the methods within research projects, and contributions to reports
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opportunity to work with whole-genome sequencing data from over 200 A. pullulans strains, isolated from wild strawberries collected from diverse geographic locations across Europe. The work will include pan
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Life Cycle Assessment applied to agricultural systems. Proven experience with field data collection in livestock or mixed farming contexts. Proven ability to publish research findings in peer-reviewed
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. In this project, you will have the opportunity to combine molecular biological methods with field studies and data analysis to enhance preparedness against forest diseases and promote sustainable