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plasticity in intestinal regeneration and cancer to contribute to the preparation of scientific publications and presentations to contribute to teaching in Biomedical Sciences (Master programme) Profile: M.Sc
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. Biomolecular condensates have emerged as a new paradigm to understand biological functions in living cells. Dresden has pioneered research in the field of biomolecular condensates and developed into its vibrant
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programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to
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PhD fellowship at the Copenhagen Center for Glycocalyx Research at the Department of Cellular and Mo
dissecting how protein and glycan assemblies drive signaling and cell communication. Develop precision tools to selectively target disease-relevant assemblies, opening new therapeutic opportunities in cancer
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Description We are seeking a motivated new PhD candidate who wants to join an exciting collaborative research program within the VIB-Center for Inflammation Research between the Guilliams and
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background checks may be conducted on qualified candidates for the position. Technology for people DTU develops technology for people. With our international elite research and study programmes, we are helping
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on studying underlying molecular and cellular mechanisms that regulates metabolism and epigenetic changes in the development of innate immune memory (trained immunity). The goal is to develop trained
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Structural Biology, Immunology, Cell Biology, Neuroscience and Synthetic Biology www2.mrc-lmb.cam.ac.uk/students/international-phd-programme/projects/. PhD students at the LMB are affiliated with
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DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and