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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Part of the research engineer's
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that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 1 month ago
. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09447 Requirements Skills/Qualifications Expected skills include Knowledge on distributed, parallel computing and numerical
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Engineering, Applied Physics, Applied Mathematics, Computer Science/Engineering, Geomatics, or a related field. You have knowledge of distributed, parallel, and high-performance computing suitable for handling
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. Dr. Tiziano De Matteis (who will be co-supervisor), is an Assistant Professor in the group. His interests are on Parallel and Distributed Computing with a focus on systems and programming models
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of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In
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Assistant Professor (tenure-track) and Associate Professor (tenured) Positions in Computer Scienc...
, theorem provers, type systems, categories, etc.), concurrent programming languages (choreographic programming, session types, etc.), distributed computing (cloud computing, microservices, etc.), Dev(Sec)Ops
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applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering practices for scientific software (version control, testing, continuous