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well as large-scale GPU computing facilities for deep learning. We are looking for a Research Engineer to manage the EEE GPU Cluster. The role will focus on enhancing the EEE GPU Cluster team’s ability in terms
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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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efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow prediction Integration of domain decomposition methods into the learning framework to enable
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond) School of Computer Science PhD Research Project Competition Funded Students Worldwide
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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, and security monitoring tools. PREFERRED: Professional certification (CISSP or equivalent), hands-on experience with securing HPC, GPU cluster, or data center environments, experience with AI/ML
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maintain the workload scheduler and architect quality-of-service policies. Administer Linux systems across infrastructure projects and deployment of new GPUs for research and teaching. Troubleshoot complex
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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 19 days ago
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 will analyze
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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