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Field
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-based and machine-learning approaches), the digital twin will provide decision-makers and industry stakeholders with actionable insights about when, where, and how corrosion risk evolves. As a postdoc
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PhD in biology, cognitive science, or an adjacent field (e.g. biomedicine, computer sciences); A strong academic track record, including high-quality publications (quantity is less important
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in R, MATLAB, Python and/or other programming languages. Experience in AI and machine learning techniques applies to physiological, neural, and imaging data. Preferred qualifications Experience with
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. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and process-based radiative transfer models
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to deploy machine learning to support data analytics and complex decision-making processes. Knowledge of modern SW-tools in the area of energy and sustainability is highly beneficial. Your role and goals You
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
prediction using large-scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and
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for large-scale NGS analysis, machine learning methods). We are looking to recruit a bioinformatics researcher for the ESCALATE project. The main task will be to test and validate a new indexing structure
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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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profile: The ideal candidate will have: A PhD in biology, cognitive science, or an adjacent field (e.g. biomedicine, computer sciences); A strong academic track record, including high-quality publications
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference