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and meteorological modeling. Experience running or maintaining operational/automated forecast systems. Experience applying machine learning methods to air quality or geophysical data (e.g., forecast
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Demonstrated experience in control theory and control system design (e.g., model predictive control, feedback control, adaptive control) Experience applying machine learning/AI to engineering systems Proficiency
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signals, imagery, and natural language, via adaptive machine learning; 2) Personalization of sequential decision making, biophysical digital twins, and the operation of digital and physical systems; and 3
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, Computer Science, Machine Learning, Artificial Intelligence, Engineering, Mathematics, Operations Research, Economics, Finance, or a closely related subject. Preference will be given to candidates with strong
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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fusion science & technology, in the Tritium Science and Technology (TST) research group at the University of Manchester. The TST group conducts research focused on tritium behaviour in materials, tritium
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discoveries made in the laboratory with meaningful improvements in patient care. You will be part of a research environment that values curiosity, creativity, scientific rigor, and continuous learning while
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating catalytic reaction mechanisms, with a focus