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Field
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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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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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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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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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: 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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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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insights into the robustness and resilience of land use decisions in an uncertain future. Relevant landscape models will be applied in selected case study countries to explore greenhouse gas flux and
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2025 Apply now In the face of ongoing climate change and sea level rise, the Dutch delta must adapt to ensure a safe and livable environment. In the past decades, the southwestern part of the delta has
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supply system for providing electricity (and heat/cooling) for the DAC plant Detailed model for DAC operation with respect to fluctuating energy supply and climatic conditions Model for assessing