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dissecting the functionality of different EV subsets. Methods include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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. Researchers at Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the
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. Researchers at Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the
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be possible, depending on the interest of the PhD candidate and availability of the pilot sites. The core activity of the ATLAST2 shall support Researchers, PhD candidates and Postdocs. They work in
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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floods—in mountain catchments. The goal is to understand long-term changes in runoff regimes and flood hazards by combining climate-driven (glacier-)hydrological modelling with reconstructions of past
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updates, and interoperability at scale. In close collaboration with project partners, the PhD candidate will focus on relevant data modeling and processing approaches for data gap filling, redundancy
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snow in local and regional climate models is poorly constrained, leading to uncertainties in estimating mass loss through sublimation and snow redistribution. The PhD candidate will develop and execute
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processes as droplets/condensates wet membrane compartments in cells. Numerical simulations and theoretical membrane models will be developed, aiming to couple viscous interfacial fluid flow, elastic