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Field
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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-heavy domains. In addition to infrastructure leadership, the Chief Architect will drive research in parallel computing, distributed systems, performance engineering, and next-generation AI infrastructure
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develop software for high-energy particle physics. Optimize software for performance, scalability, and efficiency on modern computing architectures, including HPC and distributed systems. Participate in
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physics. Optimize software for performance, scalability, and efficiency on modern computing architectures, including HPC and distributed systems. Participate in research and development activities
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develop software for high-energy particle physics. Optimize software for performance, scalability, and efficiency on modern computing architectures, including HPC and distributed systems. Participate in
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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technologies; knowledge of HPC parallel and highly performant clustered or distributed file systems architectures and their effective use and deployment for storage and management of research data lifecycles
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. Formulating necessary solutions using various parallel computing paradigms and tools, HPC schedulers (such as slurm), Containers and Kubernetes, Python, Bash and other scripting/programming languages in
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are searching for a motivated and talented Computer Science and Software Engineer to join our Applied Communications team of the Applied Research Laboratory (ARL) at Penn State. This is a software engineering
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in deep learning at scale, familiarity with the “alphabet soup” of distributed computing (DP, TP, SP, CP, EP) Experience with production environments, including Git-based workflows Experience working