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Field
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feed in scientific results for the national and international environment and climate policy. It performs multiscale research from molecular to global scale and focuses on climate change, air quality
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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, hydrology, water quality, and climate-related data. Lead data processing, ensuring data quality and consistency. Conduct water quality modeling for multiple Florida watersheds using Watershed Assessment Model
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event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components. About the LEAD AI fellowship programme LEAD AI is the University
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ecosystem modeling; downloading and processing projected future climate data; designing field experiments; procuring necessary field and laboratory equipment and supplies; conducting experiments; and
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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synthetic biology. Apart from shedding light on the fundamental aspects of transcriptional control, this project also opens new avenues for the design of climate-resilient crops. Your tasks Supported by
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to microclimatic variations induced by local vegetation. The researcher will contribute to the analysis of vegetation–climate–technology interactions to optimize energy yield in environments subject to climatic
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/01) Position Description: Apply Position Description Postdoctoral Associate in Optimization Modeling School of Civil and Environmental Engineering Cornell University The School of Civil and
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vulnerability modeling, and (c) population and built environment exposure to climate hazards. The broad agenda of this research is assessing the fitness of geospatial indicators to inform conceptual and policy