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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
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particle formation for atmospherically relevant molecules. ORCTOOL (Organic Cluster Tools) aims to create a toolbox for understanding and cost-effectively predicting the rate of massively multicomponent
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ecology predictions and empirical data on the evolution of sex-specific differences in immunity and life history traits. As we intend to conduct interviews also during the application period, we appreciate
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how natural selection shapes sex-specific immune strategies. The goal is to generate quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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with various methods. By integrating the findings with archaeological knowledge, the goal is to elucidate bacterial genetic evolution that was shaped by human influence and make predictions to the future
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knowledge, the goal is to elucidate bacterial genetic evolution that was shaped by human influence and make predictions to the future. The work provides the possibility to develop skills in microbiology, data