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Field
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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Positions Country France Application Deadline 15 Oct 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a
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for simulating two-phase flows by integrating advanced Artificial Intelligence (AI) techniques with traditional computational fluid dynamics (CFD) methods. The role focuses on transitioning legacy CFD solvers
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parallel clusters Integrate existing physical models into new software infrastructure for EOS research Benchmark against existing methods and support reproducible, open-science practices Collaborate closely
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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Université de Technologie de Belfort-Montbéliard | Belfort, Franche Comte | France | about 2 months ago
for simulating such complex geometries. For example, the memory and computation time required become prohibitive with standard “black-box” finite element methods. The objective is therefore to develop a dedicated
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opportunities for parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel
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-Performance Computing for Exascale" contributes to the design and development of numerical methods and software components that will equip future European Exascale and post-Exascale machines. This program is
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numerical model for flame radiation, based on a path-space Monte Carlo method. This approach simulates photon trajectories through a semi-transparent, anisothermal, and heterogeneous medium (including
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for industry and life. Research field presentation : The Lattice Boltzmann method (LBM) is gaining increasing interest in Computational Fluid Dynamics. While traditional methods rely on a discretization of the