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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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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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assistant professor or associate professor whose research advances parallel and high-performance computing. Relevant areas include computer architecture, operating systems, compilers and the
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 14 hours ago
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- compiler frameworks (LLVM, MLIR)
- embedded software development
- computer and accelerator architectures
- parallel and distributed computing
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, publication of scientific articles, participation in projects, etc. Demonstrable knowledge of parallel and distributed programming Languages: English at C1 level or above Other: The following will be