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Field
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feedback; optimize for real-time performance; distribute computations between GPU and device-proximal processors; ensure precise synchronization with visual and auditory stimuli. Real-Time Collision and
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institution. • Experience in developing and training machine models using PyTorch or TensorFlow in Multi-GPU environments. • Experience in working with high performance computing systems for both training and
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of individual nanostructures, with large-scale GPU-accelerated micromagnetic simulations developed within the group. The research is carried out within the DynaMat group at Ghent University. This group studies
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GPU clusters for large-scale analyses. Terms of employment The average weekly working hours are 37 hours per week. The position is a fixed-term position limited to a period of 3 years. The employment is
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programming. Experience in power systems software like PSCAD, PSS/e. Preferred Qualifications: Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs
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software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral communication skills. Motivated self-starter with the ability to work independently
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numbers effects beyond what is currently possible. • Perform mathematical and numerical analysis of tensor network methods. • Work on GPU implementation of tensor network solvers. • Disseminate research
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with multiple deployment mechanisms like Diskless, Warewulf, and traditional deployment (cobbler, PXEboot, and/or Bright). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML
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computing (HPC), distributed compute, or accelerated environments (GPUs, multi-node systems). Solid background in database systems, including: Relational databases (e.g., PostgreSQL / SQL) Graph databases
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), including model training, evaluation, and experimentation. Familiarity with distributed or accelerated computing environments (e.g., GPU‑enabled systems, shared compute clusters). Working knowledge