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Python, using GPUs), with the precise balance determined by the candidate’s background and interests. The mathematical tools involved will include matrix analysis, optimization, backward error analysis
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: metrics, configs, checkpoints, weight versioning, model registry Simulation and Testing: Run large-scale cloud experiments; track throughput, GPU utilization, cost per run; evaluate robustness to preemption
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these codes in C++ or Fortran Adopting these codes for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in
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to roll out an advisory track in Computer Engineering, we are seeking qualified experts in Computer Networks, Computer Organization, Data Analytics, Cyber Security and Privacy, Edge/Fog/GPU Computing
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performance with energy consumption reduction. Key Responsibilities: Conduct research on energy-efficient computing systems, including CPU/GPU/accelerator architectures, virtualization, and workload scheduling
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when necessary. Work closely with ODFM to facilitate request for office and lab access. Manage any IT matters, including IT equipment allocation and support for GPU servers (if required). 2. Support on
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month ago
coupling with adaptive meshes. High-order spectral finite element methods (SFEM) offer superior accuracy per degree of freedom and are naturally suited to HPC architectures (CPU/GPU clusters). Two main
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(Docker), set up CI/CD pipelines, and deploy for inference on GPU/CPU servers. Data engineering, Governance, and Documentation Manage and de-identify image and text datasets, track experiments (e.g., MLflow
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computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224 CPU cores. We analyze large public datasets
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computer hardware and network infrastructure for the ITS Research services. This covers the 12000 core, 100 GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted