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engineering Strong background in computational science, applied mathematics, or computational biology Ideally, familiarity with numerical methods for PDEs (e.g., finite difference, finite element) HPC
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80%-100%, Zurich, fixed-term The Swiss National Supercomputing Centre (CSCS) develops and operates a high-performance computing and data research infrastructure that supports world-class science in
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thinking with a structured, quality-focused approach to data and methods. Ideally, experience in one or more of the following: data engineering, building data-driven apps, computational linguistics, machine
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on poroelastic effects and induced seismicity in geothermal systems. The position focuses on production-induced seismicity in fractured reservoirs and combines field experiments, data analysis, and numerical
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degree in biomedical engineering, mechanical engineering or related relevant field A demonstrated and substantial experience with computational simulation methods, i.e. finite element analysis English
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models Lead the design and implementation of innovative methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs
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Heritage and Preservation engages with theoretical and practical challenges of methodical collecting, assessing and preserving of monuments. It is affiliated with the Institute for Preservation and
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ISSAC Newton, or Mujoco Good foundation in linear algebra, numerical methods, optimization, and control theory. An excellent knowledge (written/oral) of English is a must. Knowledge of German is
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qualifications include strong analytical and quantitative skills in numerical analysis, programming, high-performance computing, as well as skills in dynamics & structural health monitoring, data analysis and
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challenges. The work is conducted at the interface of mechanics, artificial intelligence, and computational science. The developed methods will be validated on benchmark problems and real-world data and