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Field
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programming in C++ and Python. - Mandatory mastery of GPU programming (CUDA) for optimization. - Experience with Deep Learning frameworks (PyTorch). - Knowledge of the AliceVision architecture is a major asset
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made at the Postdoctoral Research Associate rank. The AI Postdoctoral Research Fellow will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or be about to
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interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same time, the software must be lean enough to run not only on powerful
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) specifically for GPU resource management and model training. More Information Location: Kent Ridge Campus Organization: Asian Institute of Digital Finance Department : Credit Research Initiative Employee
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labs The project offers access to GPU-enabled HPC clusters, high-end workstations, and a collaborative, interdisciplinary environment. The student will develop a versatile skill set applicable to careers
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infrastructure, and GPU/CPU cluster environments. This role leads and mentors a team of Systems Engineers and Administrators while remaining deeply technical and hands-on, actively designing, deploying, and tuning
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platforms such as llm servers, shared virtual GPUs (VGPUs) used by OPS-G, and the broader utilization of cloud resources. Ensuring the smooth operation, availability, and continuous improvement
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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or similar deep learning frameworks GPU know-how: Familiar with GPU workflows and distributed training setups Data competence: Experience in preprocessing, augmentation, and dataset organization; confident
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code