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Field
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and component reliability. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s) of activity/research for the traineeship Growing mission complexity, the need for European
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observations/models and from models of social dynamics. Motivating aims include the discovery and analysis of hidden large-scale patterns in data/models, elucidating mechanisms underlying the emergence
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solutions for offshore wind turbines, enabling to enhance their structural awareness, real-time reliability assessment, and predictive maintenance decision support through integrated sensing, modelling, and
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Framework for Medical Device Surveillance https://reporter.nih.gov/project-details/11091149 · CardioOnco-AI: AI-Empowered Cardiotoxicity Risk Prediction Among Breast Cancer Survivors Using Multi-Site
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of tropical forest-climate system interactions, and works on cutting edge advancements in model predictions of tropical forest responses to a changing climate over the 21st Century. We invite outstanding early
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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of their development and structure. Working closely with colleagues at the Universities of Leeds and Reading, you will integrate theoretical understanding, observational data, and modelling approaches to improve
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for cell transplantation therapies in animal models of Alzheimer's disease, stroke, and epilepsy. To achieve this goal, the candidate will combine a gene network-based approach with a machine learning model
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Computational Postdoctoral Fellow (Quantitative Modeling Group) - 106785 Division: BE-Biological Systems & Engineering Berkeley Lab’s (LBNL, https://www.lbl.gov/) Biological Systems and Engineering
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assimilation on weather prediction Perform data-preprocessing, quality control, error modelling and correction Write reports and scientific publications Job Requirements: PhD qualification degree in Atmospheric