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Computer Science, Information Technology or a related field Proficiency with multiple Linux distributions such as Ubuntu, CentOS, Debian, and Red Hat. Hands-on experience with Git and at least one scripting
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transformer architectures (e.g., ViT/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g
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total of 108 months of related experience. Skills: Effective verbal and written communication skills. Proficient in Microsoft Office. Highly organized and able to handle multiple projects and deadlines
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). Experience with distributed systems, GPU computing, or cloud-based simulation environments. Knowledge of human-in-the-loop simulation, training effectiveness evaluation, or synthetic environments. Experience
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infrastructure, driving the design and evolution of HPC and AI platforms at scale. This role architects and implements next-generation GPU/CPU clusters, high-bandwidth InfiniBand and Ethernet fabrics, large-scale
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, managed directly by CARC staff, include multiple compute clusters that include state-of-the-art GPU resources, dedicated enterprise and high-performance storage systems, high-speed networking systems, and a
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. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures without changing the code. Our research team at NYUAD (New York
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of the Postdoctoral Research Associate includes contributing to multiple projects including resilience-aware scheduling, deep learning workload job scheduling, and storage system performance tuning. The candidate will
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preferred). Essential Functions of Position: Manage and maintain multiple GPU clusters and networked storage systems. Monitor system performance, troubleshoot hardware issues, and coordinate repairs
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 19 days ago
, embeddings with transformers, training with flow matching) and high performance computing (e.g. handling large-scale parallel simulators, multi-node and GPU training on large supercomputers). When considering