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, and/or numerical mathematics, as well as an excellent command of a programming language, preferably Python or C/C++. The candidate should have an interest in modeling and solving a complex, coupled
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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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applications. Existing research model multiple UAVs as a single shared entity in a centralized Digital Twin (DT), which poses safety risks to IAM operations if the cloud or central database becomes unavailable
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the field of model-driven and data-driven computational science. Job description Are you interested in bridging the gap between mathematical sciences on the one side and natural sciences, engineering and
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thus regulating cellular glycosylation (Voss et al . (2014), EMBO J), the laboratory has more recently employed state-of-the-art genome editing, tailored cellular models and advanced proteomics and
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-controlled laboratory area Careful style of working Personal commitment, motivation and self-discipline to carry out research independently Communication skills in an interdisciplinary research environment
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directly on our real test vehicles (see image) for automated driving. Application of modern software engineering approaches (e.g., agile methods, model-based development, CI/CD pipelines) in concrete
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hotspots, and they are exceptionally beautiful. We will use a combination of molecular tools, quantitative genetics, and population genetic modelling to study genetic diversity in DNA sequence and phenotype
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to search for spectral signatures of gamma-ray and X-ray emission from Galactic and extra-galactic objects using the most recent models for the magnetic fields along the line-of sight. Doctoral research