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Field
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engineering applications through the exact same governing equations. The software for this work is our state of the art open source multiphysics weakly compressible SPH solver DualSPHysics [3] with GPU hardware
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using clusters like UPPMAX and GPUs for high-performance computing and parallel computing using clusters like UPPMAX and GPUs for high-performance computing are essential. While not required, experience
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maintenance of codes for different platforms, including HPC and GPU systems, as well as support in the management and exploitation of massive databases. · Support for scientific projects: facilitating access
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of today’s heterogeneous hardware (multicore CPUs, GPUs, SmartNICs, disaggregated datacenters). We explore: SmartNICs & P4 switches for offloading intelligence from hosts Device-to-device communication
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. Training LLMs, large-scale deep learning systems, and/or large foundation models using GPU/TPU parallelization while setting up the environment/system network under various constraints, such as limited
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for performance, cost-efficiency, and low-latency inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions
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electromagnetic engineering problems. Programming experience in C/C++ and GPU is desired. Other qualifications being expected are as follows. Good analytical and problem-solving skills; Good analytical and
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decays and related physics object performance studies , development of the real-time analysis (RTA) in particular with ML/AI reconstruction on hybrid GPU/FPGA architecture for the electromagnetic
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computational experience, preferably on HPC systems with knowledge of parallelisation techniques and GPU programming. You will be expected to plan your own research, with guidance if required, and to assist in
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with the domain of optical material behavior acquisition at a decent pace. What you bring to the table Very good C++ programming skills GPU & Shader programming, ideally knowledge of PBR (Physically