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Field
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for GPU-accelerated applications. Data Engineering Tools: Proficiency in data engineering tools, including Apache Airflow for workflow orchestration, and message brokers like RabbitMQ or Kafka
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; and scaling and optimising data science and AI research to leverage HPC and GPU infrastructure. Main Duties and Responsibilities 1. Provide leadership of software engineering services managed and
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of parallel computing (GPUs) to speed solution within the optimisation process. Funding Notes 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant
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, Git, Azure Data Studio, pandas, NumPy, and Scikit-learn. Experience with High-Performance Computing (HPC) platforms with advanced skills in SLURM-based multi-node, multi-GPU training, data parallelism
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researchers and future Research Software Engineers. Serve as liaison with Princeton Research Computing staff on GPU cluster related issues. Professional Development Learn the underlying science, mathematics
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applications for computer vision. Experience using C++ to develop and run software applications. Experience using GPU-accelerated ML and related frameworks. Experience with Docker or other containerized
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. Experience using C++ to develop and run software applications. Experience using GPU-accelerated ML and related frameworks. Experience with Docker or other containerized application development platform Three
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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of ORNL’s AI/ML tools, leveraging high-performance computing resources and AI-focused GPUs. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity
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the entire northern sky every day and processes over 6.4Tb/s of sky data 24/7 on a large on-site GPU cluster. CHIME is a multi-institutional collaboration with 100+ members. The Scientific Software Engineer