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Memorial Sloan-Kettering Cancer Center | New York City, New York | United States | about 2 months ago
capabilities. We can access a high-performance computer cluster with the most advanced GPU resources. We also partner with the New York Proton Center, which houses one cyclotron, three rotational gantry
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with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer
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GPU utilization and allocate computing resources efficiently across users. c) create and manage user accounts for faculty and students; troubleshoot user issues; and design and deliver
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 2 months ago
17 Jan 2026 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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their efforts on the education of students and the performance of life-changing research across a wide range of disciplines including medicine, engineering, physical sciences, energy, computer science, and social
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Posting Summary Logo Posting Number FAC00001PO26 Advertised Title Department Chair - Computer Science Engineering Campus Columbia College/Division College of Engineering and Computing Department CEC
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Programme? Not funded by a EU programme Reference Number 2025-2026_4_321 Is the Job related to staff position within a Research Infrastructure? No Offer Description Offer description: A three-year position as
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support the IIT High Performance Computing systems. The candidate will be the main system administrator of four HPC clusters with a total of about 360 GPUs. Within the team, your main responsibilities will
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for performance, cost-efficiency, and low-latency inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions