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and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber experiments, samples from world-unique CO2
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estimates remain highly uncertain because existing approaches often neglect critical lake-specific dynamics and feedback mechanisms that are essential for accurately predicting ecological responses and
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incorporating machine learning to predict the influence of microstructural features on the structural integrity of metallic materials, e.g., resistance to plastic deformation and crack growth. Responsibilities
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understand and predict forest carbon budgets under climate change. There will be some flexibility in the specific work tasks and the hypotheses to test, dependent on the fellow’s knowledge, skills and
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advanced data-driven approaches and state of the art biochemical- and OMICs technologies, we strive to understand and predict the role of foods, dietary components and dietary patterns in human health. We
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predictive control under uncertainty for autonomous systems. The research aims to develop improved numerical methods for solving challenging belief-space motion planning problems, where the uncertainty in
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. To understand current biodiversity, we also need to understand its evolutionary history – to predict its evolutionary future. We aim for interdisciplinary approaches to understanding environmental change and
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granted by the Swedish Research Council (VR). The task of our computational group is to predict the intrinsic properties of magnetic materials using natural rare-earths compositions from ores discovered in
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will work on a newly started interdisciplinary project focusing on exploring the AI based generation and use of synthetic data for improving predictions of energy performance of buildings as
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useful in the past to predict actions that might be useful in the present, try them out and update its knowledge base (i.e. update learning). This postdoctoral position is funded by Wallenberg AI