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neutrophils and platelets in the setting of both sterile and pathogen-driven inflammation. State-of-the-art methods include transgenic mouse models with cell-type specific knockout mice, a broad range of
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The International Max Planck Research School for Molecular Plant Science (IMPRS MolPlant) is a doctoral programme in plant science at the Max Planck Institute of Molecular Plant Physiology and the
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highlight the potential of microbiome intervention in immuno-oncology. In our team, we actively integrate translational data, functional assays and preclinical models, to understand and overcome mechanisms
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engineered 3D hydrogels, we will experimentally probe the mechanical forces and physical constraints that drive coordinated cell behavior. In parallel, we will develop and apply computational models and
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specifically, the computational team will build on our previous work (PMID: 38951512) to establish and train deep neuronal network models on large existing dataset with multi-omic data. Subsequently
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! Overall, research in the Group of Prof. Dr. Meyer zu Hörste at the Department of Neurology combines basic models and patient-derived specimen with cutting-edge technologies. One of our recent projects has
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of additively manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon
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systems and in vivo models in combination with cell state and mutation reporters and single-cell technologies with spatial readouts to study the impact of intestinal microbes and their metabolites on colon
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infection biology, immunology, parasitology and medical biotechnology The following 6 PhD projects are available in 2026: Innate immune recognition in the gastrointestinal epithelium – Organoids as host model
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assessment of chemical plants using HAZOP analysis Use of process modeling and simulation to enhance quantitative assessments Use of machine learning to support HAZOP discussions with the aim of obtaining a