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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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distortions in the presence of a shallow crustal hydrothermal system at a model caldera volcano. The model will use the phase field approach and be implemented in Alya, the parallel multi-physics Computation
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, science, engineering, and mathematics applications used in computational computing environments. Supports management, tuning, operating systems, parallel file systems, automating deployment of compute nodes
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. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel), enhancing data locality by fusion, and blocking (i.e. accessing arrays in a way
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) to estimate progression to Stage 3 T1D. In parallel, it will dissect the mechanisms underlying this miRNA profile in human immune cells (neutrophils, macrophages, T and B lymphocytes) by modulating key miRNAs
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on representing the field of parallel programming in the Bachelor and Master study programs of TU Wien, as well as contributing to usual management and faculty service tasks. Where to apply Website https
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Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a batch HPC environment with a
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field from an accredited institution. Demonstrated skills in creative problem solving and troubleshooting Experience with parallel computing and multi-core \ multi-threaded programming libraries Knowledge
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their research in one eligible host laboratory at Nantes University : ITX-lab: https://umr1087.univ-nantes.fr CR2TI: https://cr2ti.univ-nantes.fr CRCI2NA: https://crci2na.univ-nantes.fr RMeS: https://rmes.univ
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materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning