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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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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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synaptic resilience and the reliability of synaptic responses. The work primarily involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation
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knowledge of numerical methods Experience with flying drones and drone data processing (thermal IR, multispectral, LiDAR), fieldwork experience Strong English communication skills. Knowledge
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excitations and excitonic effects using advanced Wannier-based methods * Quantum transport in polymer materials with electron–phonon coupling Full details and application instructions: https://www.ch.nat.tum.de
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, groundwater flow simulation, and numerical model development Basic knowledge of numerical methods for solving nonlinear systems of partial differential equations (e.g., finite volume method, finite element
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(beyond model training) Solid programming skills (Python required; C++/CUDA a plus depending on simulations) Interest in physics-based simulation, numerical methods, or computational engineering Motivation
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wide range of opportunities. In connection with the cooperation in one of the numerous research and working groups, we also closely cooperate with the German Institute of Human Nutrition, who
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conditions • A broad range of further education and professional development programmes (for example language courses) • An occupational health management model with numerous attractive options, such as our
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. It has close links to numerous universities and research organizations both within Germany and abroad. Our leading position in theory- and evidence-based consultation gives our employees unique access