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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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AI pipelines (similar to the attached prototype). Enable users to configure model parameters, connect modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU
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infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption). Researchers and students will explore innovative approaches to reduce
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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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(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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-meter optical/IR survey telescope, the LIGO laboratory, and a dedicated CPU+GPU computing cluster at the Massachusetts Green High Performance Computing Center. The appointment will be for a maximum of
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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
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the next generation of AI leaders, mentor students on groundbreaking research projects with access to state-of-the-art GPU hardware, forge partnerships with industry and academia, and contribute to AI
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) for reproducible research workflows. Support Optimising GPU-accelerated workloads (e.g., PyTorch, TensorFlow), including multi-GPU scaling and distributed training. Develop training materials, documentation, and
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experiments using tools such as JupyterHub, and Kubernetes. Experience designing and operating massive-scale GPU and combining CPU/GPU workloads across these services. Design and debug platforms and will work