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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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rheology, fluid dynamics or bioprocess engineering, and a strong desire for multi-disciplinary work. The candidates should be fluent in written and spoken English, enjoy working within the interface between
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, chemical engineering, environmental engineering or biotechnology with a strong background in rheology, fluid dynamics or bioprocess engineering, and a strong desire for multi-disciplinary work
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laser physics and/or plasma and fluid mechanics is an asset, especially if combined with strong hands-on laboratory skills Programming experience, particularly in Python, is welcomed Strong verbal and
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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microswimmers/microrobots that can move and interact autonomously in 3D environments, mimicking the complex dynamics of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable
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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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, 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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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