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Field
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optimization, intelligent runtime systems, code generation and transformation, HPC, parallel and distributed computing, compiler infrastructures, heterogeneous systems, and autonomous system optimization
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for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing
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, and reproducible code development skills; distributed or parallel computing is a plus. Experience designing and executing field experiments in urban or environmental settings, with willingness to
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across Linux environments, programming and scripting languages, job scheduling, scientific applications, optimized compilers, mathematical libraries and algorithms, and distributed and parallel file
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systems and servers. Documenting system administration procedures for routine and complex tasks. Technical Environment: Linux build automation in a large, distributed computing environment with Puppet
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benchmarking of parallel and distributed computing systems and of the workflow systems that run on them. Familiarity with the FAIR data and research software principles, and with producing AI-ready
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, Electrical Engineering, Mathematics, or a closely related field. Strong programming proficiency in Python and C or C++. Experience with parallel and distributed computing using technologies such as MPI
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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
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HPC concepts, including parallel computing, distributed systems, and optimization. Analytical skills, problem-solving abilities, and a growth mindset. Additional Qualifications Applicants should be
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assistant professor or associate professor whose research advances parallel and high-performance computing. Relevant areas include computer architecture, operating systems, compilers and the