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for anisotropic laminae and laminates (e.g., layer-wise / higher-order plate models) to accurately predict stress fields and assess cloaking performance. Build a staggered multi-scale simulation workflow (from
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fundamental physical model to understand the process of fire spread for wildfires, as part of the European Research Council grant FIREMOD: (https://cordis.europa.eu/project/id/101161183 ). This is a full-time
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have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models for Alzheimerâ™s, Parkinson, and Dystonia as well as to identify novel
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nuanced bedside observations can meaningfully inform model predictions. The resulting model will be rigorously evaluated using cross-validation and a held-out dataset, and then tested prospectively in a
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analytical approaches and technological tools (e.g., artificial intelligence, remote sensing, environmental informatics, predictive modeling, and/or environmental genomics). Research should address pressing
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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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diversity expected under different conditions of resource competition. The post-doctoral fellow will develop new modeling frameworks, using R or a related language. Where to apply E-mail positions@gimm.pt
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developing models that predict the effects of variants. We tackle this challenge via two main directions: (1) developing efficient pangenomic data structures and evolutionary models, and (2) designing deep
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the Norwegian Institute for Nature Research (NINA) and partners in 14 countries. For more information, see: https://seatrack.net . This is a fixed termed position for 3 years in our section for terrestrial
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal