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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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of the molecular ISM of galaxies, with a focus on galaxy centres. In particular, this project aims to measure the spatially resolved properties of giant molecular clouds and/or weigh the supermassive black holes
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of the molecular ISM of galaxies, with a focus on galaxy centres. In particular, this project aims to measure the spatially resolved properties of giant molecular clouds and/or weigh the supermassive black holes
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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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Applications are invited for a Postdoctoral Researcher in High-resolution Modelling of Aerosol-cloud interactions to assess Marine Cloud Brightening. This position is part of the are provided by
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reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing Knowledge
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the Biomass Combustion system and RPB-CO2R systems in a unified model. Develop a cloud-based web simulation tool that combines a simplified numerical version of the REUSE system. Key Attributes required: A
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cloud data and 2D floor plans to assess spatial and visual properties of EDs, ensuring data accuracy through ground-truthing. Apply cluster analysis and statistical methods to classify EDs into spatial