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structural components iv. Data acquisition, instrumentation and monitoring during laboratory testing Perform numerical modelling and simulation, such as finite element modelling, parametric studies, and
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derived theoretical frameworks from the European Research Council – Starting grant ‘OceanCoupling ’ project and numerically implement the theoretical models. Interested applicants must address why and how
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We are recruiting full-time Research Fellows to develop hybrid physics-AI methods for weather applications Available data include: • Numerical weather prediction (NWP) model outputs
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of algorithms for medical image analysis. The successful candidate will investigate computational complexity, stability, and numerical summaries of multiparameter persistence, as well as applications to medical
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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and numerical tools, conduct feasibility studies and elaborate realistic proposals and will contribute to the data analysis of the experiments possibly inspired by the theoretical work. They will
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investigate computational complexity, stability, and numerical summaries of multiparameter persistence, as well as applications to medical image analysis, and participate actively in the research environment
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About the role As a postdoctoral research fellow, you will participate in an industry-funded project and perform numerical studies, develop tools facilitating the interaction of finite element
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++, or Julia, with strong numerical modeling, data analysis, and reproducible code development skills; experience with Git-based workflows and AI or ML for science is a plus. Experience designing and executing
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signal processing, imaging, inversion, or source characterization, is highly desirable. Proficiency in Python, C++, or Julia, with strong numerical modeling, data analysis, and reproducible code