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- Eindhoven University of Technology (TU/e)
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- Delft University of Technology (TU Delft); Delft
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mathematical modeling; fundamental understanding of fluid mechanics and soft matter physics; good quantitative skills and strong analytical capabilities; proven experience with experimental image and data
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engineering, or in a closely related discipline; a strong and proven affinity with experimental techniques and mathematical modeling; fundamental understanding of fluid mechanics and soft matter physics; good
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Vacancies PhD position on the design and fabrication of MEMS drag force-based flow and fluid composition sensors Key takeaways In this project, we will combine well-known thermal flow sensing
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. Candidates with experience in high-voltage / plasma research are preferred. Knowledge of computational fluid dynamics is a plus. Strong organisational and communication skills are expected in order to
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to the proportion and composition of mineral, melt and fluid phases across a range of geologically-relevant pressure, temperature and composition. With constraints on the partitioning of trace elements among
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: reduced-order models (ROMs) and input-output models derived from high-fidelity Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter
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to the proportion and composition of mineral, melt and fluid phases across a range of geologically-relevant pressure, temperature and composition. With constraints on the partitioning of trace elements among
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have experience with experimental work? Are you challenged by building (parts of) an experimental setups? Knowledge on fluid mechanics, physical transport phenomena and/or phase changes are considered
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collaboration with DENS solutions company. Femto-Cryo will use 3D printed fluid force microscopy (FluidFM) cantilevers from TU Delft, and CryoSilico cryo-EM sample supports from DENS solutions to develop a novel
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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on