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Field
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are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what
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intersection of atmospheric science, machine learning, satellite remote sensing, and climate policy.
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nitrogen dynamics, and climate change mitigation potentials in agroecosystems. You will be contributing specifically to the area of regional simulation using process-based models and advanced statistical
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an impact on climate via cloud formation. The MACS project aims to understand the role of mixotrophic protists in Antarctic C- and S-cycles. Such mechanistic understanding will help predict how changing ice
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effectively to increasing climate threats particularly through a focus on digital transformation and data. One particular goal is to ensure that solutions are not only scientifically robust and practically
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 2 months ago
observation-driven paradigms to develop and learn novel representations of the coupled ocean-atmosphere dynamics ocean models. For accurate climatic predictions, it is essential to have plausible forecasts
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 22 days ago
measurement capabilities for studying planetary chemistry, habitability, astrobiology, and climate evolution. In addition, researchers in exoplanetary science are strongly encouraged to apply as well. We
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policy design, implementation, and governance on a broad range of topics as established by, for example, the Climate Agreement, the Environment and Planning Act, and the Act to strengthen participation
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a cornerstone of UM6P’s commitment to addressing the critical challenges of agriculture, water management, and climate change in Africa. Established to operationalize the Agriculture, Water, and
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. Preferred Qualifications Experience and expertise in integrating multi-scale remote sensing data (spectral or structural) with other environmental variables (e.g., climate, soil, and ground observations