266 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" Postdoctoral positions in Sweden
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, https://avengers-project.eu) and IM4CA (https://im4ca.eu). Work duties The main duties involved in a post-doctoral posistion is to conduct research. Teaching may also be included, but up to no more than
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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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promote sustainable agriculture. More about the Department: https://www.slu.se/en/departments/ecology/ More about working work at SLU: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala, Sweden
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of working with motion capture, eye tracking, machine learning, or other advanced behavioral analyses or related research experiences. A consistently excellent academic track record is required, including
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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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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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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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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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, 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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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