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Field
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). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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interfaces (APIs), servers and other web resources open to the broader scientific community. Support the on-site HPC Linux GPU cluster with Slurm queuing system. Maintain RAID NFS storage, Lustre storage
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systems. Areas of interest may include, but are not limited to: - Advanced accelerator chip technologies, such as GPUs or other specialized chips for large-scale AI processing - High-speed memory
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interfaces (APIs), servers and other web resources open to the broader scientific community. Support the on-site HPC Linux GPU cluster with Slurm queuing system. Maintain RAID NFS storage, Lustre storage
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) and parallel computing, with a focus on cost-efficient and scalable model deployment. Skilled in working with medium-large scale multicore and heterogeneous (CPU + GPU) clusters. Excellent verbal and
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methods (LBM). For fluid simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation
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dedicated to research, advanced medical data streaming and processing machines, as well as state-of-the-art local and scalable cloud-based, compute infrastructure (CPU, GPU). Long-standing and very successful
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research on wireless sensing and communications, and a GPU Lab for training of advanced machine learning models. IDLab is both part of the University of Antwerp and the research centre imec. The University
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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full