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Field
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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the group, translating cutting-edge deep learning algorithms into robust, maintainable analysis tools and pipelines Implement and optimize large-scale deep learning workflows on GPU-enabled High-Performance
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simulation, Cosserat rod theory to support hands-on class activities and development of NVIDIA GPU-based course modules. • Availability to schedule, coordinate, and oversee student visits to operating rooms
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, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary The Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory is seeking to appoint two highly motivated
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with AMD MI300A GPU+CPU. 2. Perform benchmarking studies to enhance scalability and achieve high node-level efficiency, surpassing existing AMR frameworks. 3. Contribute to communication optimizations
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for advanced research. A7. Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Desirable: B1. Knowledge of Trusted Research Environments out with or within an HPC
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or machine learning infrastructure, model deployment environments, or GPU-enabled systems. Familiarity with CI CD pipeline design and automation. Experience with hybrid infrastructure spanning on-premises and
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selection criteria Good oral and written presentation skills in Norwegian, or another scandinavian language Experience with the CUDA programming model for general-purpose GPUs Experience with the OpenCL
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practices and international standards.Leverage VIB Data Core services, including high-performance computing pipelines and large-scale GPU resources, to scale ML development and deployment. Your profile PhD
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substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus of the position is on research, aiming at publications in