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to apply Website https://www.academictransfer.com/en/jobs/357846/post-doctoral-researcher-digita… Requirements Specific Requirements Responsibilities and tasks: Employment of state-of-the-art tools to run
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of multi-fidelity and active learning strategies for molecular systems. The candidate will collaborate in an international research team on related research questions in machine learning, uncertainty
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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
of uncertainty quantification techniques for the learnt models. You will also have opportunities to contribute to open-source computational tools and datasets, teach master-level courses, and advise doctoral
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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
systems as well as towards designing observer-based state estimators from output timeseries data measurements. The research also involves development of uncertainty quantification techniques for the learnt
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience