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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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Experience in organising and analysing human user trials. B9 Experience with a modern machine learning environment, including use of GPU clusters and modern ML tools and JAX. B10 Experience in working with
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 3 months ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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performing routine maintenance on lab computing resources (e.g., Linux servers, GPUs, networked workstations). (10%) Performs other tasks, duties, and responsibilities as required Minimum Qualifications
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research. The role offers exceptional
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environments, cloud computing, or GPU-accelerated machine learning Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation Familiarity with biological sequence alignment