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Field
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expertise in interdisciplinary mathematical modelling, inverse problems, finite element methods (especially in complex and applied settings), machine learning techniques in scientific and industrial
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(variational multiscale, multiscale finite elements, etc.), structure preserving numerical methods, stochastic optimization, analysis of machine learning methodologies, multilevel methods, scale-bridging and
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in computational mechanics and a strong background in solid mechanics and the finite element method. Candidates should have a demonstrated experience in teaching undergraduate and/or graduate courses
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methods (e.g., AFM, Nano-IR, STM, SECCM, SICM, SECM), advanced light and electron microscopy, materials synthesis (e.g., conductive metal-organic frameworks, 2D/hierarchical structures, nanoparticles
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the muscle architecture of the larva within the generative process. We recently extracted the muscles of the Drosophila larva body from a CT-scan recording. Furthermore, we developed a finite element
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an Innovate UK-funded KTP project (assessed at application) Desirable criteria Experience of working on high voltage power electronic systems (assessed at application/interview) Experience with finite element
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outlined in the Job Purpose, and a comprehensive theoretical and practical knowledge of the mathematical theory of continuum mechanics and associated finite-element numerical methods implemented in ABAQUS
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computational mechanics and numerical modeling methods such as finite element analysis (FEA) and multi-body dynamics for hydro-mechanical drilling systems Proficient in structural dynamics modeling and system
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with other disciplines for developing deeper insight into the physical behavior of materials and structures, such as through combinations of finite element methods with machine learning. Tasks also
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. Extensive experience in the development and application of finite element method (FEM) or comparable methods for AM applications. Preferred Qualifications: Demonstrated expertise in multi-physics simulations