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metrics and usage statistics, identify inefficiencies on different levels (CPU/GPU, I/O patterns, etc.) and provide corresponding reports. You will work closely with researchers and HPC users and provide
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environment. This includes a HPC cluster with NVIDIA H100 nodes, parallel storage and fast interconnects, dedicated workstations with multiple A6000 GPUs, and web-based services via Microsoft Azure
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computing resources, with additional support involving HPC systems such as configuring GPU nodes, managing Slurm queues, containerising teaching notebooks, and enabling advanced pipelines Promote Robust
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architectures Analysis and preprocessing of different data types (texts, images, time series, graphs, etc.) Review latest literature and data Orchestration and documentation of deep learning experiments
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research. Strong technical expertise in large scale data analysis, statistical and simulation tools, machine learning, AI and GPU programming. Experience with one more of these scientific collaborations
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stunning countryside. Our talented scholars and researchers from around the world are tackling global issues and making a difference to people's lives. We believe that inspiring our people to do outstanding
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, e.g. HPC cluster (CPUs and GPUs) or cloud. Proficiency with high performance computing and system architecture. Advanced skills and experience associated multiple of the following: artificial
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and GPUs) or cloud. Proficiency with high performance computing and system architecture. Advanced skills and experience associated multiple of the following: artificial intelligence; method and machine
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management application GPU & specialized hardware for Artificial Intelligence and Deep Learning Server-class hardware and remote management The HPC administrators are responsible for working closely with
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in