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of metabolic systems may define the possible interactions. The postdoctor's role will be to use mathematical models and metabolic models to study the evolution and dynamics of these microbial interactions
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) models. We are interested in building models that are explainable and are extracted from complex and heterogeneous data. Within explainable ML, we are interested in topics as provenance, interpretable and
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models that capture this richness, uniting synthesis and analysis with the help of both discrete and neural models. Covering both synthesis and analysis in one framework also allows us to create efficient
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having an immense impact on both society at large and research especially, and this impact is expected to increase. This boom is driven by so-called deep neural networks, a class of machine learning models
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, to improve our mechanistic understanding of plant acclimation to heat and cold stress. The project will mainly work with the root of the model organism Arabidopsis thaliana and will combine state-of-the-art
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objective is two fold: i) to develop a novel high-throughput RNA sequencing technology tailored for single bacterial cells, and ii) to apply it on a model system (Yersinia sp.) in order to better understand
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. Doctoral degree in biochemistry, cell biology, chemical biology, molecular biology, or in another relevant field. Additional requirements are: Good command of written and spoken English. Merits: Experiences
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microbes make decisions, bioinformatic techniques to compare these mechanisms to empirical data, and game theory and modeling approaches to improve our understanding. The project has many exciting directions