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the group can test and validate models of sodium-ion battery behaviour. As such, proficiency in computer programming (particularly python) is required as well as knowledge of numerical methods
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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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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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, turbulence, CFD, numerical methods, and turbulent combustion. • Proficiency in HPC environments (parallel computing on CPU/GPU systems) • Demonstrated publication record in reputable journals/conferences
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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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working on related topics Participate in teaching and supervision activities, in line with the candidate's profile and interests The research activities will be hosted by the Parallel Computing and
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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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. 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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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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chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU