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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
, neural operators are combined with classical numerical methods to efficiently approximate parameterized finite-element models. By integrating data-driven and FEM-based approaches, hybrid models are
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strengthen your application: Experience in deep machine learning, finite element modelling, biomechanics, and anatomy What you will do Take courses at an advanced level within the Graduate school of
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electromagnetic fields using the finite element method, electrical measurement technology, accelerator physics, electrical engineering You enjoy working in a small team of specialists, seeking a constant exchange
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and advancement of multi-fidelity finite element models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual
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learning for material characterisation and property prediction, finite element modelling, structural optimisation, and experimental testing of timber, bonded interfaces and laminated components. The
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/courses or thesis project with high grades) in continuum mechanics, the finite element method and the mechanics of fibre-reinforced polymers. Interest and high skills in programming and numerical methods
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high grades) in the finite element method and the mechanics of fibre-reinforced polymers. Interest and high skills in programming and numerical methods Strong written and verbal communication skills in
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exploit them. This PhD will explore high-frequency (>1 MHz) power converter design, with a focus on achieving improvements in power density. This includes developing novel magnetics, utilising finite
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simulations using the method of Finite Differences in the Time Domain (FDTD), determination of optical material constants by multiangle UV-VIS-NIR ellipsometry, and/or characterization by UV-VIS-NIR
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to continuum, dislocation dynamics, phase field, crystal plasticity finite element method, physics-informed digital twins, and/or data-driven and deep learning-based digital twins. Stipend: The