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the outstanding scientific environment of the Beutenberg Campus providing state-of-the-art research facilities and a highly integrative network of life science groups. We offer a multifaceted scientific project
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collaborations. The Leibniz-HKI is embedded in the outstanding scientific environment of the Beutenberg Campus providing state-of-the-art research facilities and a highly integrative network of life science groups
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international networks with universities, research institutes and industrial companies Outstanding facilities and infrastructure Flexible working hours and mobile working Your application: We welcome applications
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, clinicians, and computational biologists in Tübingen and within our international network (US, CH, NL) Key publications from our group Wimmers F, et al. Multi-omics analysis of mucosal and systemic immunity
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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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activities aiming at developing soft skills and at strengthening networks and collaborations in academia and industry. Be part of a strong postdoc community. The Umeå Postdoc Society fosters networking and
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of the Microverse” (www.microverse-cluster.de ), the CRC/Transregio 124 “Pathogenic Fungi and Their Human Host: Networks of Interaction” (www.funginet.de ) funded by the Deutsche Forschungsgemeinschaft and the
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environment of the Beutenberg Campus providing state-of-the-art research facilities and a highly integrative network of life science and technical science institutes and groups Strong scientific collaborations
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, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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computational models to map co-expression networks and predict systemic disease transitions. Characterise intestinal microbiome changes and their correlation with inflammatory diseases. Computational modelling