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Field
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différents types de parallélisme, que ce soit au niveau d'un nœud de calcul (CPU et GPU) qu'au niveau d'une grappe de PC. Cet environnement inclura les outils nécessaires à la description et à la construction
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will also collaborate with a postdoctoral researcher and another PhD candidate on creating new GPU-enabled pharmacophore searching algorithms. Prospective validation will be achieved by predicting
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Language Model (LLM) training platform, developing unified capabilities for GPU resource pooling, training job scheduling, inference acceleration and the Machine Learning Operations (MLOps) platform
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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-field code, written in the Cuda C language and parallelized on a single GPU (Graphical Processor Unit). A parallelization on multiple GPUs would be a welcome development during the thesis. Where to apply
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programming (Python, C++, etc.) and machine learning and signal processing libraries; You have HPC/GPU computing experience, including running deep learning workloads on compute clusters (CUDA-compatible GPUs
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/GPU environments. Provide consultative support and training to researchers using BRC AI/ML tools and pipelines. Performs related duties & responsibilities as assigned/requested. Qualifications REQUIRED
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., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with team members
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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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that serve the entire campus community. You will bridge the gap between high-performance hardware and practical user applications, ensuring that our AI infrastructure, from GPU infrastructure to sovereign data