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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 19 days ago
GPUs using ROCm, HIP, and related translation tools. REQUIRED: Bachelor's degree or equivalent with a minimum of five years of work experience in either deeply technical fields and/or computational
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fields and/or computational research experience; strong proficiency in Python and C++, with deep familiarity with AI/ML frameworks (PyTorch, TensorFlow, JAX); hands-on experience with GPU programming
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well as large-scale GPU computing facilities for deep learning. We are looking for a Research Engineer to manage the EEE GPU Cluster. The role will focus on enhancing the EEE GPU Cluster team’s ability in terms
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Internal Number: 25597 INFORMATION SECURITY MANAGER, The Massachusetts Green High Performance Computing Center (MGHPCC ), to serve as the primary security leader across MGHPCC and the AI Computing Resource
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Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge. To address
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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4 Apr 2026 Job Information Organisation/Company CNRS Department Laboratoire de physique de la matière condensée Research Field Physics Chemistry » Computational chemistry Researcher Profile First
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studies. These pipelines must be capable of efficiently exploiting different types of parallelism, both at the level of a computing node (CPU and GPU) and at the level of a cluster of PCs. This environment
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compensation and working conditions. Basic Qualifications Ph.D. or M.D./Ph.D. in areas such as machine learning, computer science or closely related field. Excellent programming skills and practical experience
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programming (Python, C++, etc.) and machine learning and signal processing libraries; You have HPC/GPU computing experience, including running deep learning workloads on compute clusters (CUDA-compatible GPUs