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Field
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XR facilities including VR/AR headsets, GPU workstations, robotics, 3D immersive wall and 3D motion tracking. The successful candidate will advance digital pedagogy, and design and deliver impactful
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familiarity with Deep Learning, including PyTorch would convey a significant advantage. They will have access to our in-house GPU-enabled High Performance Computing platform as well as national HPC resources
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provided with the working material (working station, PC and access to local GPU clusters) as well as with a discounted rate for the close-by Inria canteen, together with a gross salary of 2650 euros/month
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/GPU architectures and even quantum computing are currently actively explored. Candidates are sought that align with these research areas. A broad interest in teaching topics would additionally be
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high-performance computing capabilities (including approximately 4,000 Nvidia RTX 4000 Ada GPUs and over 30,000 CPU cores) hosted at the project data center in Nevada where the telescope is located
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documentation, and promote the benefits of our HPC resources across the institution. Collaborating closely with academic staff, researchers, and IT Services, you will ensure the HPC and GPU facilities
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structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and applied machine learning/deep
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big data technologies (e.g., Hadoop, Spark). Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud). Familiarity with data governance and compliance standards. Familiarity with CPU/GPU
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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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. Experience with algorithm design, embedded DSP development, multithreaded programming, GPU development, SDR hardware platforms, FPGA development, and/or Linux-based designed tools is desired. Representative