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Field
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tasks and meet competing deadlines by using judgement and initiative. Desirable Skills and Experience (Advantageous but not Essential): Experience with high‑performance compute (HPC) or GPU‑accelerated
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(including numerical optimisation, variational methods and MCMC sampling) C3 State-of-the-art deep learning (including transformers, graph neural networks and normalising flows) C4 Use of GPU programming and
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: ED431F 2023/40 Project Title: Searching for Physics Beyond the Standard Model at LHCb Using Neural Networks and GPUs Funding Period: Start: 1 January 2023 End: 31 December 2027 Principal Investigator's
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their understanding and utilization of HPC resources. KNOWLEDGE Required Knowledge of a variety of HPC systems (CPU, GPU, storage systems, file systems, networking, virtualization, job schedulers (Slurm) and scientific
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-day reliability and long-term evolution of clustered compute, GPU resources, high-performance storage, and the scheduler/tooling that support research at scale. Drives a culture of operational
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working on the LHCb experiment at the Large Hadron Collider. The successful candidate will focus on developing LHCb’s real-time data analysis systems, including the GPU-based software trigger. This work
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, particularly oncology, genomics, imaging, or clinical NLP is a plus, but not required Experience with scalable ML infrastructure, multi-node GPU training, or local/private deployment settings We offer A full
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for a researcher in gravitational wave astronomy at the Institute of Applied Computing and Community Code (IAC3, http://iac3.uib.es ) funded by the Beatriz de Galindo fellowship (BG24/00137) supported
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with staff ID card For more details, please see: https://hr.utexas.edu/prospective/benefits and https://hr.utexas.edu/current/services/my-total-rewards Must be authorized to work in the United States
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. Serve as a liaison with Princeton Research Computing staff on GPU cluster-related issues. Professional Development Learn the underlying science, mathematics, statistics, data analysis, and algorithms