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practice (e.g., Python/MATLAB/C++ and/or established modelling platforms). Familiarity with asymptotic and multiscale mathematical analysis methods to ground proof numerical simulations Strong communication
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Method (FEM) Numerical simulation (e.g. Abaqus, UMAT) and coding experiences (Python, Fortran) are a must Additional Information Benefits We offer a position in an inspiring, multidisciplinary and
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international community of scholars and fellow postdocs and benefit from a world-class seminar series. The Center also hosts numerous workshops, conferences, and other academic events that foster collaboration
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/ applied mathematics (PDEs) with an experience in coding & numerical methods. A basic background in fluid mechanics/ free surface hydraulics would bring added value Additional Information Work Location(s
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numerical optimisation, matrix calculation, linear regression and the development of mathematical models and quantitative methods for the optimisation of industrial production, for the management
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, numerical methods for multiphysics problems, and the development of numerical optimization methods for virtual design of metamaterials and structures. Do you want to be part of a dynamic research environment
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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to contribute to research on hybrid numerical-exponential asymptotic methods and their applications at the University of Sydney Base Salary, Academic level A $105,117 - $121,054 p.a + 17% superannuation About the
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pipelines by combining statistical, synthetic, and decentralized methods to improve both the privacy and utility of anonymized datasets. Moreover, this research will examine the potential of AI and LLM
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for motivated students in the field of numerical fluid mechanics and multi-physics simulation methods. Be part of change Analyze and optimize existing coupling strategies to improve the efficiency and scalability