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Field
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studies. These pipelines must be capable of efficiently exploiting different types of parallelism, both at the level of a computing node (CPU and GPU) and at the level of a cluster of PCs. This environment
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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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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
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Computer (HPC) with its sizable cluster of CPUs, GPUs, storage arrays and high speed networking as well as our cloud (AWS) based Data Safe Haven, used for researchers working on sensitive data. As Research
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to support efficient model training iteration; (b) lead the construction of the GPU computing cluster centered around a Kubernetes + NVIDIA GPU Operator, including node planning, resource management
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modeling). Experience working with HPC/GPU resources and job schedulers (e.g., Slurm) and/or cloud-based deployments. Track record of contributing to peer-reviewed publications as a computational specialist
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-performance computing systems, GPU acceleration, and parallel file systems - Ability to communicate fluently in English, both spoken and written Additional qualifications - Knowledge of or interest in
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, procurement, and deployment of High-Performance Computing (HPC/GPU) clusters and high-capacity data storage infrastructures in strict compliance with Spanish and EU public procurement regulations. Data
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critical role in defining the future of research and administrative computing at the University. In this role, you will lead the architectural design and deployment of secure, scalable AI platforms
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(URCF) at Drexel University is building a new shared computing platform focused on GPU-accelerated workloads, particularly AI model training. The system includes GPU and CPU compute nodes with Nvidia H200