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-throughput processing. Enhance the computational efficiency of compute environments by optimizing resource allocation (including CPU/GPU utilization), parallelizing data pipelines, and resolving processing
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infrastructure, including GPU-based systems. Also of importance are: Demonstrated teaching proficiency Type of employment: The position is fixed-term for four years. During the period of employment, an Associate
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to a postdoctoral post, including a rapidly growing AI security lab, a large-scale GPU datacenter containing state-of-the-art foundation models, as well as direct collaboration with an award-winning AI
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scanning, virtual reality and on-demand wide-area surveillance video feeds. In addition Durham University hosts the UK regional supercomputer Bede (128 NVIDIA V100 + 3 NVIDIA Hopper GPUs) in
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based
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GPU-centric communication techniques. Collaboration & impact: We actively encourage you to publish your research in high-profile international venues, providing the opportunity to contribute directly to
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, optimization, or high-performance computing is highly desirable Experience with quantum software frameworks (e.g., Qiskit, PennyLane, Cirq) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered
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acceleration. Practical expertise in real-time control systems or adaptive feedback loops. Hands-on skills in data acquisition systems and high-speed communication protocols. Competence in GPU computing
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at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective bargaining and matters affecting your
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compute hardware and network infrastructure for the ITS Research services. This covers the 12000+ core, 100+ GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted