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for magnetic particle transport in fluid flows and validating the results through a laboratory-scale reactor. It combines numerical modeling, AI methods, and hands-on experimental work in the context of advanced
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analysis" led by Theresa Lange . The group investigates the interplay between randomness and convection in fluid flows. For this work, we are looking for a PhD Student. You will work on rigorous analysis
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to defining specifications and requirements for the FDI system • Development and evaluation of model-based and machine learning-based FDI algorithms, in close exchange with relevant stakeholders • Close
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Modeling for Hydrogen Storage (m/w/d, E13 TV-L, 75%) Responsibilities: Develop a pore-scale model for two-phase flow of hydrogen gas and brine Develop a bioreactive-transport model and couple it with the two
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quantify the influence of geochemical reactions on flow regimes and thermal transport across pore and Darcy scales, Collaborate with project partners for model validations, applications, and predictive
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experimental models with patient-derived specimens. Utilizes cutting-edge technologies Long-standing interest in advancing the understanding of human neuro-immunology Focus on analysis of: Cerebrospinal fluid
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messengers transported by the flow or even the pressure of the fluid itself. In an interdisciplinary team, you will either develop theoretical models of the feedback between flow and network architecture
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sound background in geophysical fluid dynamics, experience in numerical ocean or atmospheric modelling, and experience with numerical data analysis. Good scientific presentation, writing, and