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Field
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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schematics, layout, test benches and simulation Design blocks as Op-amp, LDO, VCO, PLL, voltage or current reference circuits Develop innovative analog design solutions to enhance signal integrity, noise
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graduate school for (MD/)PhD training with a first-rate international reputation. It coordinates teaching and ensures excellent training in high-quality research laboratories, and it actively encourages
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of Parasitism”, which includes a variety of theoretical, simulation, and experimental projects from different disciplines. The key goals of the project are to elucidate physical mechanisms that govern trypanosome
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for calculation and design Design and optimization of a flexible oxygen supply Optimization of the pilot plant based on test and simulation results Development of design and process engineering solutions
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workflows for descriptor based microstructure reconstruction to identify material parameters for crystal plasticity simulations from experimental data through inverse analysis to establish structure–property
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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experience growing, managing, and phenotyping plants in the field and greenhouse. You have experience using modern approaches in root phenotyping, image analysis, or simulation modeling to understand
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less than eight semesters, three references are required; please use the official DAAD template [doc-Datei] and ask your professors to email the confidential document to the GSPoL. Step 2: personal