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Field
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environments CPU/GPU accelerated computing HPC cluster architecture High-speed interconnect technologies Parallel and distributed storage Workload management and scheduling Configuration and provisioning
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, artificial intelligence, computer networks, graphics, parallel and distributed systems, mobile systems, machine learning, Cybersecurity and secure computing, data science and AI, and theory. Our faculty are
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distributed computing runtime with application and compilers to efficiently integrate quantum processors with HPC resources. The research will explore different quantum computing modalities, their
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quickly and functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed
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optimization fundamentals, both computational and mathematical. Preferred Qualifications Familiarity with distributed computing frameworks such as MPI and OpenMP Experience with Pyomo and/or JuMP Experience
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(Docker, Kubernetes), and robust debugging techniques. Experience with cloud platforms (AWS, GCP, or Azure) and parallel computing, with a focus on cost-efficient and scalable model deployment. Skilled in
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Intelligence, Machine Learning, Data Science, Security and Privacy, Parallel and Distributed Computing, Deep Learning, Internet of Things (IoT), and Algorithms. Successful candidates will hold a
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storage hierarchy, especially filesystems operating systems, runtimes, and resource management compilers and programming systems distributed systems and parallel computing architectures hardware
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resources to researchers at the University of Chicago. It is dedicated to enabling research by providing access to centrally managed High-Performance Computing (HPC), storage, and visualization resources
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; PyTorch; parallel and GPU computing Applicant Profile The internship is intended for a final-year Master’s student or engineering student specialising in applied mathematics, signal processing or