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with additional biophysical features Apply the framework to morphogenetic problems based on imaging data Run large-scale simulations on ETH Zurich’s high-performance computing (HPC) infrastructure
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broader research agenda focused on how digital infrastructure and computational tools can support (or hinder) progress toward effective, coordinated, and equitable biodiversity governance. Research topics
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-ceramic composites in close collaboration with a PhD student at the Biomaterials Engineering Group at ETHZ and the identification of process-structure-property relationships enabling the efficient design
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. Utilizing a combination of experimental and computational approaches, we develop and characterize novel functional materials and devices driven by robust nanoscale quantum effects. We are currently seeking a
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focuses specifically on using and refining the ICON model in Large-Eddy Mode (ICON-LEM) to simulate the cloud seeding experiments conducted during the project and improve process-level parameterizations
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tomography (APT) and high-resolution scanning TEM for atomic-level microstructure and chemical composition analysis. Computational Modeling: Micromagnetic and molecular dynamics (MD) simulations. Job
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degree in physics, computer science, mathematics, computational neuroscience, or related fields. Extensive knowledge of dynamical systems theory. Excellent programming skills in Python. Previous experience
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(IVT) at ETH Zurich intends to develop scalable optimization systems for operational support in large-scale road networks. Modeling and simulation are powerful tools for the development and validation
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product quality attributes, such as remaining shelf life. Process measured sensor data of commercial cold chains, analyze data for variability, and reformat data in databases. Use the simulation-based
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learning-based generative models and physics simulation) and data inference (including segmentation, classification, parameter inference and mesh fitting) based on data-driven and (bio)physics-informed