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Field
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Researcher in experimental soft matter physics. Specifically, the candidate will set up microfluidic triple-phase flows with a liquid crystal elastomer (LCE) precursor as middle phase which is flown either as
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insights into coronary anatomy, blood flow, pressure, and wall shear stress—together with information about model uncertainty—the project aims to support more precise, physiology-guided treatment decisions
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to further develop concepts in nonlinear model reduction Possible research directions include the development of nonlinear modelling and control approaches for fluid-structure interactions, material with
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highly interdisciplinary and involves engineering (e.g., fluid mechanics), materials science, chemistry, and physics. Different organic materials are used, including polymers (e.g., stimuli-responsive
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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
for scientific staff: www.inside.dtu.dk/en/human-resources/during-employment/salary/salary-structures . The period of employment is 3 years. Starting date is 1 February 2027 or according to mutual agreement
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matter and physical chemistry of interfaces. The physical and chemical properties of fluid interfaces decorated by surface-active species (surfactants, proteins, particles) are central to food engineering
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cells isolated from peripheral blood and cerebrospinal fluid samples. Carry out cellular and molecular biology experiments, including B-cell isolation and culture, flow cytometry and molecular analyses
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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Computational Fluid Dynamics (CFD) has become indispensable for aerospace design, many important problems—including high-fidelity flow simulations, uncertainty quantification, and multidisciplinary optimization
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
(www.code-saturne.org ), EDF developed recognized expertise in neural-network-based learning for handling simple fluid flows with high accuracy [Meyer et al., 2021]. In 2022, an algorithm hybridizing POD