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include the development of finite elements methods, as well as inverse design strategies based on deep-learning and Neural Networks approaches. The latter will then bring the project to the experimental
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Engineering or a closely related discipline. You will have demonstrable experience in one or more of the following areas: Finite Element Analysis, Computational Fluid Dynamics, Discrete Element Method, Multi
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the University, will provide meaningful support for your teaching and scholarship as elements of your personal and career development. We encourage you to grow professionally, and we support a healthy work-life
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ministerial journal list, 4) experience in numerical modeling of single-step sintering processes of thermoelectric modules using the finite element method, 5) experience in 3D design and experimental
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(including, but not limited to, phylogenetic analysis, morphometrics, or finite elements modelling) are expected. Extensive participation and / or experience in leading fieldwork is expected. The successful
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sizing. Familiarity with practical finite element analysis using ANSYS Workbench or Comsol, and pipe stress analysis programs such as CAESAR-II, CAEPIPE, AUTOPIPE, WINPIPE, ROHR2. Experience creating and
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phase-field methods within finite element frameworks with advanced experiments in simulated nuclear reactor water environments to predict material lifetime. The project includes materials characterization
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. The work combines material modeling using advanced plasticity theories and nonlocal micromechanics-based fracture models in a finite element framework to predict material degradation and failure. The project
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research methodology is coupled CFD (Computational Fluid Dynamics) and FEM (Finite Element Method) modelling and simulations. This is the only methodology allowing simulations of fluid-structure interaction
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 2 months ago
to develop a digital twin of the process. The approach is to couple phenomenological models obtained by AI processing of experimental data with Finite Element Models (model reduction by AI) and Cellular