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metrics for reducing the environmental impact of AI and cloud computing Empirically evaluate different parts of the AI lifecycle, including development (training), operation (inference) and use Develop
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to: Identify and evaluate carbon metrics for reducing the environmental impact of AI and cloud computing Empirically evaluate different parts of the AI lifecycle, including development (training
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interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with
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. Design, test, and document computational frameworks that combine 4D point cloud data, geospatial analysis, and advanced ML/DL algorithms. Integrate dynamic environmental datasets into immersive and
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, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and
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, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and
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postdoctoral researcher with a background in politics, law, economics, or related disciplines to work on GEOCLOUD: The Geopolitics of Cloud Computing , a European Research Council Advanced Grant project