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for a duration of six years. NumPEx contributes to the design and the development of numerical methods, software components and tools that support future productive European exascale and post-exascale
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physics Physics » Thermodynamics Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 19 Apr 2026 - 23:59 (Europe/Brussels
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
job scheduling, resource allocation, and parallel computing. Special Physical/Mental Requirements N/A Campus Security Authority Responsibilities Not Applicable. Special Instructions Quick Link https
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Louis Lions. - Design and implement innovative methods for the numerical solution of wave propagation problems within the FreeFEM software, using high-performance computing - Optimize the code
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 months ago
computational efficiency without compromising numerical accuracy. In particular, since HDG methods rely on high-order polynomial approximations, special attention will be given to optimizing quadrature strategies
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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learning architectures for scientific or high-performance computing applications. Background in software performance evaluation, profiling, and optimization on CPUs and GPUs. Knowledge of common numerical
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validation experiments for modelling • Computational fluid dynamics techniques • Finite element analysis method • Reviewing literature, planning and managing research, writing technical report / paper
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and