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algorithms to explore creative new hand designs, and reinforcement learning for policy optimisation, all within a huge GPU-based simulation with thousands of robots learning synchronously in parallel. The best
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suite of software, and its deployment on the university HPC & GPU based system. The position is primarily research and enterprise, but there would be a contribution of up to 20% to teaching, including
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of ORNL’s AI/ML tools, leveraging high-performance computing resources and AI-focused GPUs. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity
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be expected to utilise or support the development and enhancement of our fire modelling suite of software, and its deployment on the university HPC & GPU based system. The position is primarily
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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bioinformatic pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation and, in some instances, clinical reporting
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computing (HPC) systems, including CPU, GPU, storage, file systems, networking, visualization, job schedulers, and scientific applications Experience leading the implementation and execution of research
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(CNNs, vision transformers), multi-GPU multi-node training, and federated learning strongly encouraged. Experience working with cloud platforms. Experience with high performance computing. Publication
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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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especially on GPU infrastructure enhancements and improvements as part of Yale’s comprehensive campus investment in AI . As an experienced subject matter expert, you will help lead the system design