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Field
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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
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, or related discipline; A relevant background in fluid mechanics and experimental methods; Hands-on experience with modern experimental methods for flow measurement is preferable; Affinity for working with
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candidate with: A Master’s degree (or equivalent) in mechanical engineering, applied physics, or related discipline; A relevant background in fluid mechanics and experimental methods; Hands-on experience with
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of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks
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fluid dynamics) Course organisation The curriculum of the IMPRS SusMet contains several scientific and non-scientific elements and a close supervision. The elements of the curriculum bring the community
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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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and Innovatie (TKI) project in a public-private partnership (PPP) scheme in collaboration with DENS solutions company. Femto-Cryo will use 3D printed fluid force microscopy (FluidFM) cantilevers from TU
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks for CI, HTI, and urban environments. The network
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metamorphic conditions, the exact mechanisms (dissolution–precipitation vs. dynamic recrystallization vs. mechanical transport vs. partial melting), the extent of mobility and role of fluids remain debated
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks for CI, HTI, and urban