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manufacturing processes, especially for the transportation sector and medical technology. To this end, materials are tailored and manufacturing processes are designed to conserve resources – from the modeling
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gases. As energetic particles propagate through air, they interact with the air molecules and produce radicals, ions and excited species which can alter the chemical composition of the atmosphere
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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from computational engineering and computer simulation (such as the finite element method and constitutive materials models) to represent and exploit relationships along the composition-process-structure
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the properties of composite, biological networks which consist of stiff filaments and liquid inclusions which arise from liquid-liquid phase separation. We will use a multi-scale approach bridging scales from
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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
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research to advance the understanding of vertical coupling processes between the lower and upper atmosphere as part of our team. The role involves investigating how dynamical and chemical processes in
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modelling is a valuable tool to revealing the source of UTLS aerosols, the origin of water masses, and formation processes of cirrus particles. Your key responsibilities include: Preparation, operation, and
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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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laboratory (bio-)chemistry, beamtimes at synchrotrons, data analysis and comparison to expected physics. Due to the complex structure of the composite droplets, we expect emerging effects which can contribute