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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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validated on a wastewater recycling pilot plant. Suitable methods for process analysis and characterization will be extended and the chemical-physical relationships will be studied using molecular and
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-Hartmann) 02 Developing Dynamic Digital Twins of the Human Heart (Supervisors A. Loewe, S. Krumscheid) You will have one supervisor from the mathematical sciences and one from the applied discipline. You
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Association. At the Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), we investigate the fundamental biological processes that drive aging. Our research combines molecular, cellular, and systems-level
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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for a highly motivated PhD candidate to join our world-leading research program in Earth System modelling and improving Earth System Modeling by better merging of measurement data and model simulations
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of records, a copy of or a link to your Master thesis (or a draft thereof), published articles or other relevant material such as letter(s) of recommendation (if applicable). Our Offer: We work on the very
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modelling and improving Earth System Modeling by better merging of measurement data and model simulations. This PhD project focuses on improving how we estimate key parameters in land-surface and ecosystem
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(transcript of records), a copy of your Master thesis (or a draft thereof), published articles or other relevant material such as letter(s) of recommendation (if applicable). Our Offer: We work on the very