270 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" Postdoctoral research jobs in Sweden
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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international research collaborations to generate cutting-edge research and outreach to further ecology as science and promote sustainable agriculture. More about the Department: https://www.slu.se/en/departments
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on the long term globally distributed WaRM experiment (for details see here: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9396 ). The employment is fulltime for three years. The deadline
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forest growth, survival and biodiversity for the future use and conservation of forests. More information: https://www.slu.se/en/wiforce . The work aims to generate new knowledge and contribute
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multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early-stage plant stress. This project
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distributed WaRM experiment (for details see here: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9396). The employment is fulltime for three years. The deadline for applications is March 26, 2026 and the
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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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, and demonstrated ability to develop computational pipelines for biological datasets. Experience in statistical modeling and/or machine learning applied to biological systems, with the ability to link
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technologies. The OEM group is part of the Laboratory of Organic Electronics (LOE) (https://liu.se/LOE ), an internationally renowned research environment comprising more than 150 researchers from diverse