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, and their applications. The successful applicant is expected to complement the faculty’s ongoing research activities in the field of applied and computational PDEs. Current research covers a wide range
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of analysis of partial differential equations (PDEs). The PhD candidate will be supervised by Havva Yoldaş and co-supervised by Raphael Winter (Cardiff University). The research topics primarily revolves around
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partial differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered, candidates with prior experience in theoretical physics, fluid mechanics, kinetic
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- Physical modelling (pde, phase field, linear stability analysis) - Immunochemistry Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR7057-CARPHI-037/Default.aspx Requirements Research
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of experimental and simulation data. Foundational research in Machine Learning for partial differential equations (PDEs). Innovative algorithmic and methodological approaches to AI-augmented HPC application
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science. It offers a novel perspective on understanding and managing uncertainty in large-scale infrastructure systems. For more details, see https://research.chalmers.se/en/project/12873 Who we are looking
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., Maxwell solvers, FDTD/FEM, coupled-mode theory, or PDE/ODE solvers) Interest in electromagnetic wave propagation, photonics, or semiconductor physics Experience with scientific programming and data analysis
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the experimental lab (https://www.mathbioleiden.nl/software.html#virtualleaf ). Incorporate detailed insights into the model of the mechanical properties of cell walls based on experiments and small-scale
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in teaching, and the contact information for three professional references to https://gannon.peopleadmin.com/postings/. Review of applications will begin immediately and this position will remain open
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experience working in one or more of the following areas: • Mathematical Control Theory • Optimization • Numerical Solutions of PDEs • Computation and Machine Learning and/or related topics. Candidates