11 high-performance-computing-postdoc research jobs at Swedish University of Agricultural Sciences in Sweden
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satellite imagery, drone imagery, introduction of native legumes into leys, biodiversity, new forage species, perennial dual use crops, performance of long-term crop rotations with leys, and forages deep
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methods, technologies, and materials to reduce reliance on unsustainable practices and fossil fuel-based compounds as a response to societal requests for green alternatives that maintain high performance
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leading to production and harvesting in the regulated countries moving to unregulated countries. The position is part of a joint research program between Swedish University of Agricultural Sciences (SLU
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rotation forestry towards continuous cover forestry methods is debated in Scandinavia as a way forward to increase biodiversity and climate resilience. This postdoc project will be based on empirical field
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Do you have a background in natural sciences, with a keen interest in interdisciplinary studies of crop sustainability? We are looking for a postdoc, in the project Diversification with cover crops
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ecosystem functioning. The postdoctoral project will focus on analyzing and modeling tree architecture in relation to forest vitality and stress predisposition. The work will combine 3D structural data (from
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datasets for analysis. Implementing and improving deep learning models for detecting and mapping forest disturbances. Validating model performance using reference datasets and ground truth information from
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rotation forestry towards continuous cover forestry methods is debated in Scandinavia as a way forward to increase biodiversity and climate resilience. This postdoc project will be based on empirical field
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in breath. The postdoc is part of a collaborative research project entitled ‘Blood, breath and bugs’ funded by the Carl Trygger Foundation. About the position The project aims at unequivocally
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for multiuse purposes, addressing issues on climate change adaptation and high-versus low intensity forestry. We use empirical and process based modelling, with input data from the National Forest Inventory and