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narratives to engage with more powerful actors towards transformative change. In addition, this project aims to include the element of ‘surprise’ by engaging with the non-linear dynamics (feedback loops and
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incorporate it into mathematical models of trait evolution across phylogenies. The work combines dimensionality reduction and geometric data analysis with the development of statistically rigorous comparative
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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Description Are you fascinated by how viruses overcome biological barriers to infect a cell? Do you have a strong interest in advanced microscopy, single-particle tracking or computational analysis? Are you
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processes. Projects can include assembling, sharing, integrating, and advanced analysis of large amounts of data from diverse sources, including experiments, observations, and simulations, to gain a deeper
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assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models
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focus in multidisciplinary research. The CMCB laboratory aims more specifically at developing cutting-edge data/image analysis as well as modelling strategies to answer fundamental biology issues with
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. Required competencies: Strong background in bioinformatics (e.g., R, Linux, Python). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS
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). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS. Excellent English communication skills, both written and spoken. Meritorious (preferred