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-performance computing, GPU acceleration, automated benchmarking or large-scale numerical workflows. Evidence of self-motivated contributing to collaborative research outputs, open-source software, preprints
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computing pipelines and large-scale GPU resources, to scale LLM development and deployment. Your profile PhD in machine learning, computer science, computational biology, computational neuroscience
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solutions on modern HPC and GPU-accelerated systems. This role includes supporting climate and geophysical science applications, enabling large-scale AI training and inference workflows, and contributing
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expertise, and substantial GPU and high-performance computing resources. You will be encouraged to contribute ideas, shape new analytical directions and participate in publications, conference presentations
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for government and commercial sponsors. A particular strength is real-time RF system development leveraging in-house FPGA, GPU, and SDR platforms. Importantly, SDD has successfully transitioned multiple
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). The applicant’s experience must be related to the tasks to be conducted, to the job description herein or to: · Training of multimodal models based on deep learning and GPU computing. · Evaluation and deployment
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methods on accelerated or heterogeneous computing platforms, including GPUs, FPGAs or other specialised hardware, for real-time or large-scale physics applications. We welcome candidates whose work is
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Tech. ARC currently operates compute resources comprised of • 50,000+ CPU cores • 500+ GPUs • over 10 PB of storage • a world class immersive visualization lab. We continuously evaluate emerging research
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. Experience with embedded systems and hardware/software integration in a research or prototyping context. Experience with FPGA/RTL development and Xilinx tools, and CUDA coding for GPU acceleration. Knowledge
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systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU acceleration is a plus. Strong leadership, communication, and documentation skills.