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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
performance of complex AI research workloads on state-of-the-art hardware. The role will have heavy focus on optimizing existing NVIDIA GPU-based workloads for top-tier AMD GPUs, such as MI355X and beyond and
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managing GPU-enabled infrastructure (NVIDIA GPUs, CUDA, multi-GPU systems) in cloud and/or on-prem environments. Familiarity with GPU orchestration in Kubernetes (e.g., NVIDIA device plugin, GPU scheduling
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initiatives and process improvement. Experience provisioning and managing GPU-enabled infrastructure (NVIDIA GPUs, CUDA, multi-GPU systems) in cloud and/or on-prem environments. Familiarity with GPU
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in chemistry, physics, or related field. At least 2 years of experience developing quantum Monte Carlo algorithms. Strong problem-solving and analytical skills. Python programming experience. GPU
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and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with clinicians and clinical trial/implementation
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inclusive workforce Access to our GPU-accelerated HPC cluster and laboratories with cutting-edge sequencing technologies and molecular assays Performance-based remuneration and other benefits The opportunity
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FLAME-GPU Accelerated Agent-based Modelling of Material Response to Environmental and Operational Loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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website to learn more about current research projects. The successful candidate fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 21 days ago
, vulnerability management, and security monitoring tools. PREFERRED: Professional certification (CISSP or equivalent), hands-on experience with securing HPC, GPU cluster, or data center environments, experience
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Language Model (LLM) GPU cluster to ensure stable and reliable operation of training tasks; (b) handle GPU node failures, IB network anomalies, CUDA/NCCL errors and Kubernetes scheduling failures, perform