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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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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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Description Reliable monitoring and control of water systems is essential to protect water resources, ensure hygienic standards, and enable sustainable infrastructure operation. As challenges evolve
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, chalcogenides, and semiconductor compounds, aiming to understand and control their growth. Analyze the deposited films and structures using scanning probe techniques and other complementary characterization
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research on biomedical sensor applications (biomedical sensor interfaces/ integrated mechanical strain sensors) analog/Mixed-Signal Integrated Circuit Design (CMOS, low-power, low-noise design) publication
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compounds in Brassica vegetables and to understand the role of glucosinolate degradation for drought stress tolerance. Tasks include planning and conducting plant cultivation experiments under controlled
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scattering experiments with free electron lasers offer a new route to the structure determination of biomolecules. Due to the super-low signal-to-noise-ratio, computing the structure from such data is
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well as analytical methods are an advantage Willingness to handle unsealed radioactive materials within a radiation-controlled laboratory environment Knowledge of handling radioactive materials and radiochemical
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with shallow water equations). Python coding for workflow control, data pre- and post-processing as well as model calibration and validation. High-performance computing (HPC) for running test cases and
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree