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estimates remain highly uncertain because existing approaches often neglect critical lake-specific dynamics and feedback mechanisms that are essential for accurately predicting ecological responses and
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well as contributing to the development of predictive in vitro models for hazard identification. Additional duties include sample collection and characterization of airborne particle emissions at industrial sites
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incorporating machine learning to predict the influence of microstructural features on the structural integrity of metallic materials, e.g., resistance to plastic deformation and crack growth. Responsibilities
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and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber experiments, samples from world-unique CO2
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. To understand current biodiversity, we also need to understand its evolutionary history – to predict its evolutionary future. We aim for interdisciplinary approaches to understanding environmental change and
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analysis or predictive modeling of pathogen biology or host-microbe systems for which multidimensional, genome scale experimental data are now available, or it may use population scale genetic, clinical