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materials. The Computational Mechanics of Building Materials (CMBM) aims to understand and predict mechanical failure in materials and structures. By combining numerical modeling tools with tailored
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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
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to advance their career by contributing to the Chair of POM. The Chair of POM focuses solely on empirical research. Candidates with a mathematical modeling/Operations Research background are discouraged from
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. Experience with running numerical models is an advantage. Experience in working in a Linux/Unix high-performance computing environment is a plus. Good communication skills in English (written and spoken
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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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The position is part of the project Accurate Modeling of Dark Matter – Electron Interactions in Liquid Xenon and Argon, a Swiss National Science Foundation collaborative project with the group
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in teaching. Profile Doctoral degree in biology, computational biology, applied mathematics, physics or equivalent Proven track record in infectious disease modelling, quantitative biology or related
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100%, Zurich, fixed-term Are you an ambitious postdoctoral scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and data processing? We invite you to
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related research directions in the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural
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team where everyone contributes their own expertise and personality. We operate with flat hierarchies and offer flexible working hours as well as family-friendly models. Our team benefits from numerous