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consisting of PAT and mechanistic / data driven modelling allowing process control. Steps to be taken will be: Developing a process applicable PAT method (single / multisensoric) for AAV / LNP / VLP detection
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). The goal of this collaborative project is to model the entire lifecycle of offshore wind turbines—construction, operation, and decommissioning—within a comprehensive digital twin framework. Your tasks As a
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position as Research Associate / PhD Student (m/f/x) in Theoretical Biological Physics (Modeling sarcomere self-assembly) (subject to personal qualification employees are remunerated according to salary
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applied engineering aspects of spaceflight systems modeling and motion analysis, with an emphasis on advanced visualization techniques for both rotational and orbital dynamics. The research will address
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are an advantage: femtosecond laser and diagnostics, high power lasers, ultrahigh vacuum, programming skills (Labview, Python) Ability to work closely within a team: engineers, students, postdocs and scientists, and
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well as Mobility Modeling and Simulation. Research topics range from conceptual issues of strategic planning to small-scale analyses of specific street spaces. The chair’s methodological focus is on the empirical
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transformation." The project will investigate the relationship between road safety and safety culture. A theory-based model of road safety culture will be developed and validated through surveys in various
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Description The Chair of Transport Modelling and Simulation at TUD Dresden University of Technology is investigating models, algorithms and simulations to achieve an enhanced scientific
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equal opportunities Support in balancing work and family life Extensive advanced training opportunities Occupational health management offers Flexible working time models Opportunity to work remotely The
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quantification, model-order reduction, or multi-fidelity methods. The primary fields of application are life science, medicine and health, earth observation and robotics. Consequently, a MUDS student will learn