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to electrochemisty and electromagnetics. Interface tracking/capturing methods such as immersed boundary method, level-set method, volume-of-fluid, phase filed method are options for model development.
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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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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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and simulation of PV systems and other offshore renewable energy technologies; hydrodynamic and fluid dynamic systems; programming and numerical modelling (e.g. Python, MATLAB); computational fluid
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computational fluid dynamic simulations can currently resolve spatial and temporal scales of industrially-relevant turbulent flows within days/weeks. On the other hand, typical design cycles in industry still
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with high-performance computing (HPC), parallel computational fluid dynamic simulations can currently resolve spatial and temporal scales of industrially-relevant turbulent flows within days/weeks
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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
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of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable capabilities: they swim, adapt, interact, and self-organize into dynamic patterns. Understanding and replicating these life
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conjugation (prodrug attachment) by biorthogonal chemistry Characterization of drug loaded nanoparticles in buffer and biological fluids Drug release studies What you bring The Barz lab is looking
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acids Solid Phase Peptide Synthesis NCA/NNCA polymerization Drug conjugation (prodrug attachment) by biorthogonal chemistry Characterization of drug loaded nanoparticles in buffer and biological fluids