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climate impact. Moreover, emissions from aircraft significantly degrade air quality, resulting in approximately 16,000 premature deaths each year. Given the anticipated increase in air traffic, the aviation
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increasing demands for climate neutrality and sustainability. It is imperative to build, demolish, and rebuild with a clear purpose and in a smarter way. A substantial share pf the global energy usage is
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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combination with multi-fidelity response models. The multi-fidelity models may include combinations of physics-based response models, Artificial Intelligence (AI) models and probabilistic methods. Your
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As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and
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project “ComDisp: Community-Centered Modeling of Housing-Related Health Disparities.” ComDisp develops a grassroots modeling framework to predict health disparities under different climate change scenarios
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computational modeling. The key objectives of ComDisp are: • Identifying and understanding housing, air quality, and respiratory health issues in each case study. • Linking climate change models to housing
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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Your Job: Maintain, and update quantitative methods for assessing economic impacts of the energy transition at the national and regional levels Develop dynamic and multisectoral economic models
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these experiments with numerical modelling can enhance our understanding of the microphysical and thermodynamic parameters governing the fragmentation process and its implications for cloud microphysics and climate