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Strong background in computational science, applied mathematics, or computational biology Ideally, familiarity with numerical methods for PDEs (e.g., finite difference, finite element) HPC experience is a
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The successful candidate will hold a PhD in applied mathematics, and will have knowledge of PDE discretization methods such as finite elements, finite volumes, etc. Practical knowledge of at least one programming
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computational models (e.g., Convection-Diffusion Equations, Finite Element Methods, Computational Fluid Dynamics) to investigate cerebral blood flow (CBF), neurovascular coupling (NVC), and neurovascular system
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of the ERC NEMESIS Synergy project (see https://erc-nemesis.eu/ ). The aim of this project is to build an integrated computing chain based on "Discrete de Rham" methods, the main feature of which is to
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of the fabricated fibers within optical networks. - Design of innovative hollow-core fiber structures using the finite element method. - Fabrication of specific optical fibers on a fiber drawing tower. - Linear
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both diffusive and nondiffusive simulations including hydrodynamic transport effects with a Finite Element solver. The final goal is to identify deviations from macroscopic diffusion in a range of
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someone with experience of running finite element models (FEM) of soft tissue mechanics. It is essential that the successful applicant can directly translate mathematical models to code (rather than
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publications (45%); - Experience in CFD simulation and finite element software for the analysis of building components; energy and thermal simulation; knowledge of digital technologies for geometric definition
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or morphodynamic applications in estuarine and coastal environments.. Proven experience in scientific programming of numerical methods (the finite element method in particular) is a strong asset. Please submit any
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
discovering research and potentially pursuing a PhD. Expected skills • Solid background in numerical methods (PDEs, finite elements, scientific computing). • Interest in modeling, model order reduction, and