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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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mathematics, ecology, history, climatic and medical sciences in collaboration across multiple institutes. An integral part of the project is to develop process-based eco-epidemiological models considering
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biogeochemical responses. However, modeling these dynamics globally remains computationally challenging. To address this, our research employs advanced computational methods to simplify high-fidelity 1-D
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, their interactions with hosts and the environment, and how they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modeling of pathogen biology or host
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science-related applications. Explicitly accounting for symmetry is not the standard approach in machine learning – a well set up model should be able to ‘detect’ the symmetry automatically. One way
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these questions, we will determine RNA structures in vivo using cutting-edge transcriptome-wide RNA structure probing techniques that together with computational models and machine learning algorithms will generate