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exposure, enabling systematic analysis of killing dynamics. Using this approach, we have shown that bacterial survival is strongly shaped by the bacterial genetic background and predicts treatment outcomes
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these areas under warming climate after the ice ages? These are central questions for both Biogeography and Quaternary Palaeoecology, and the answers provide the basis for predictions of ecosystem and species
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biological mechanisms is necessary to establish mechanistic models for tree distribution and growth that will improve our predictions of tree species' range-shifts as well as productivity changes within
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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., CAP, Soil Monitoring Law, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term
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questions for both Biogeography and Quaternary Palaeoecology, and the answers provide the basis for predictions of ecosystem and species response to future climatic change. We are looking for PhD candidates
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Law, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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. Empa is a research institution of the ETH Domain. Your tasks Optimizing vehicle aerodynamics to reduce transportation emissions, understanding airborne disease transmission, and predicting climate
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with an additional behavioral data stream. How would you evaluate whether this new modality genuinely improves predictive performance? For further information, please contact Prof. Catherine Jutzeler