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chemistry; climate and atmosphere-ocean dynamics; cloud microphysics, severe storms, and mesoscale meteorology; data assimilation, machine learning, and causal discovery; education and human resources
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system will organize and synthesize disparate datasets, identify physically consistent constraints, and recommend data assimilation configurations tailored to specific scientific objectives. All AI
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as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the globe. Candidates with relevant prior research experience
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physical and AI models This position is intended not merely to apply existing AI models, but to advance research that integrates AI prediction models, generative models, data assimilation, and ensemble
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modeling approaches. Modeling research can focus on physical process studies or data impact (data assimilation) studies. Candidates may contribute to development and coordination of current and
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Science and the Head of Conservation at LCSD to foster understanding and assimilation of results and approaches of scientific research in the arts among curators, conservators, university professors and
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. Competencies: Expertise in uncertainty quantification and statistical methods. Experience with data assimilation techniques. Experience applying machine learning approaches to ocean or coastal modeling
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for Conservation and Science and the Head of Conservation at LCSD to foster understanding and assimilation of results and approaches of scientific research in the arts among curators, conservators
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for Conservation and Science and the Head of Conservation at LCSD to foster understanding and assimilation of results and approaches of scientific research in the arts among curators, conservators
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. The candidate will develop a Bayesian data assimilation framework built around the Q-model using large datasets (e.g. Holisoils, LUCAS, ICP Forests) and scaling the results up to European level. The