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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference
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at Birmingham (UAB) and other sites in this large, nation-wide study. Requires a flexible, non-traditional schedule to meet participants’ scheduling needs and research protocol goals. Coordination of support
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The Data Analysis Intern will have the opportunity to gain valuable hands-on experience in a fast-paced and collaborative work environment, assisting with the creation, documentation, and support of
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focuses on the rigorous statistical and probabilistic foundations of machine learning and data science. We emphasize computational methods for large-scale data and scalable inference techniques. Our current
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focuses on the rigorous statistical and probabilistic foundations of machine learning and data science. We emphasize computational methods for large-scale data and scalable inference techniques. Our current
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of these complexes, using the latest approaches at all stages, including making grids, data analysis and model building. Applicants must have a PhD (or equivalent experience) with a strong background in protein
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large-scale parallel filesystems, high-speed InfiniBand/Ethernet fabrics, containerized workloads, and GPU-accelerated compute. Partnering closely with the Director and HPC Architect, the manager shapes
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of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to NCDV’s data science architecture to identify and build efficiencies and added value. Specifically, the candidate will structure and
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-specialist users. This will require extensive scientific data analyses with regard to socially relevant issues. Furthermore, large amounts of data from different disciplinary contexts must be compiled and
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-class events programme that engages our global community of alumni, donors and supporters. The portfolio will include a diverse range of in-person, hybrid and digital activities—spanning large-scale donor