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network of collaborators and strong access to compute through national GPU systems (NAISS, e.g. Berzelius and Arrhenius) and local GPU infrastructure. Project description The position offers significant
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promising habitable exoplanets around solar-like magnetically active stars? Join us to build next‑generation GPU‑accelerated models of stellar dynamos and connect them to exoplanet discovery and space
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Position Description An Associate in Research position is available in the Interpretable Machine Learning Lab ( https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in
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AI models on HPC and GPU-based systems Provide guidance on performance optimization, scaling, and efficient resource utilization Contribute to architectural and design decisions in cloud-native HPC
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open source framework for adaptive mesh refinement, written in CUDA and supporting one-sided communication for scalability. The target architecture is a supercomputer with AMD MI300A GPU+CPU. 2
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single-mode and multimode semiconductor laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where
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large-scale GPU resources, to scale ML development and deployment. Your profile PhD in machine learning, computer science, computational biology, statistics, or a related field.Knowledge of modern
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benefits can be found 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
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benefits can be found 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
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pipelines for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods