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and archival data, as well as working towards future observing proposals and strategies. Within this framework, the successful candidate will have flexibility and freedom to tailor the project
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
focused on quantifying the role of cities in global climate mitigation. This project integrates satellite remote sensing, Integrated Assessment Models (IAMs), and large-scale geospatial data to evaluate and
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on X-ray scattering data (e.g. powder diffraction, pair distribution function (PDF), single-crystal), preferably with experience in situ/operando characterization and large user facilities such as
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term role to support a large project looking at developing outdoor thermal comfort models for children? If so, we would welcome your application to this full-time, fixed term role. As a Post-doc Research
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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using experimental data from laboratory setups Validate models through targeted measurements and structured testing procedures Analyse loss mechanisms and system efficiencies under various operating
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large multi-dimensional datasets using statistical tools such as positive matrix factorization (PMF) and cluster analysis Investigate the influence of different urban emission sectors on atmospheric
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. The position will work with researchers to manage community programs, develop and monitor research protocols, analyze data, prepare reports and presentations, and disseminate information to a national audience
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research on a project to harmonize phenological data from sources such as the National Phenological Network, herbaria, and the National Ecological Observatory Network and perform analyses to understand
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly