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scenarios and impacts, or machine learning/data science applied to environmental problems. Project background Successful participants could use coupled global (CMIP) simulations, design and set up new model
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, through improved approaches to urban design and planning, water-sensitive design, soil engineering, and environmental science. It also considers the interplay with an ageing population, and emerging
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of Health Science & Technology (D-HEST). The ETH Laboratory of Sustainable Food Processing uses a combination of innovative sustainability assessment and emerging food production approaches to target
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and graphical modelling for health monitoring through wearable and remote sensing. Focused on developing forward patient status simulations with multimodal, multiorgan, and environmental information
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techniques to explore design alternatives and optimize TW configurations for maximum resource efficiency and lifecycle performance. Conducting life cycle assessments (LCAs) to quantify environmental impacts