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and design of the research plan You will be working with an interdisciplinary set of tools and approaches covering molecular biology, chemistry, materials science and physics. You will be involved in
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molecular dynamics simulations and in silico screening to assess inhibitor-target interactions and predict selectivity. Clone, express, and purify top candidates using high-throughput bacterial systems and
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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and MSc students. In the project you should: Design and implement enzyme libraries using generative AI tools such as RFdiffusion2 or BoltzDesign. Perform molecular dynamics simulations to assess enzyme
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bioinformatic analysis of NGS data. Hands-on experience in generating high-quality ChIP-seq data. Hands-on experience in culturing mammalian cells. Experience in molecular biology techniques, including use
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on understanding and controlling the structure of the casein micelle (CM), a key component in dairy systems, under various simulated processing conditions. The project will be caried out in close collaboration with
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of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts are supported by
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experienced in working with in vivo animal models, preferably models related to perinatology You have strong laboratory-based skills related to molecular biology, neuroscience, immunology You have experience
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and reduction of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts