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Postdoctoral Research Associate in Sum-Frequency Generation Microscopy of Biomolecular Self-Assembly
structure formed in model phospholipid membranes. Beyond this, the project aims to expand such investigations to bilayer systems and also investigate the influence of lipid-protein and protein-protein
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alters immune responses against vaccines using next-generation human tissue and organoid models. Current vaccines against infectious diseases and cancer benefit only a subset of patients, partly due to our
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for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The purpose of this position is to conduct research in the field of model
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computational tools to evaluate models of biological and artificial vision, as part of the DFG-funded Collaborative Research Center (CRC) “Robust Vision” at the University of Tübingen. This is a unique
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assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
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-Neptunian objects for the various suggested theoretical models. The developed workflow should also be applicable to the other discovery science projects in Dynaverse. Your tasks in detail: Perform scientific
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. Job description: - first-principle modeling and simulations of electrolytes - development of new machine learning strategies and quantum simulation approaches - application of specially developed
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Registry (SCNIR), enabling clinically driven translational research. We employ state-of-the-art experimental models to study the pathomechanism of severe congenital neutropenia and Shwachman-Diamond Syndrome
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-dimensional cell cultures are important to enable realistic cell environments for disease modeling and to analyze cell interactions. This position will address the question of how one can develop minimally
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological