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implementation and applications in other scientific and engineering domains. Job description To reliably use simulation-generated predictions in science and engineering, one needs trustworthy mathematical models
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/or quantifying the performance envelope, robustness, and safety properties of perception systems that encompass learning-based models. Develop methods and tools for automatic/procedural generation
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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-scale screens to study fundamental principles in molecular and complex trait genetics using microbes as model systems. Our core technology MAGESTIC (https://doi.org/10.1038/nbt.4137 ), a CRISPR/Cas9-based
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question: « what makes our brain human ? » (Vanderhaeghen and Polleux, Nat. Rev. Neurosci. 2023). We combine cutting-edge approaches such as pluripotent stem cell models of human corticogenesis, human-mouse
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trials (e.g., diet, FMT), and ex vivo gut models enabling advanced multi-omics analyses of these samples. In addition the lab also maintains a large culture collection, partially linked to genomic data
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electrophysiology to translational models, including animal studies and analyses of human tissue samples. This full-stack methodology enables us to directly link molecular channel function with disease phenotypes
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular