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Field
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 12 days ago
well as to candidates with expertise in parallel and HPC computing combined with a solid understanding of combinatorial optimization. While prior HPC experience is an advantage for candidates from optimization and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 12 days ago
of leveraging task-based parallel and distributed programming techniques in the DDF-pipeline software components with the aim to accelerate such processing on high-performance heterogeneous computing platforms
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integration, documentation, and collaborative development. Familiarity with parallelization and high-performance programming techniques such as MPI, OpenMP, multiprocessing, or distributed computing in Python
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Computing Systems (PCS) group at the University of Amsterdam performs research on the design, programming and run-time management of parallel and distributed computer systems. The modeling, analysis and
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comprises research and teaching at bachelor's, master's and doctoral level in computer science, primarily in operating systems, compiler construction, systems programming, and parallel and distributed systems
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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is BTH's largest department, with just over 70 employees. The department conducts research in computer science, covering the subfields of big data and AI, parallel computer systems, visual and
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/Qualifications Technical Skills: Processing and analysis of large volumes of data from numerical simulations. Parallel programming standards in distributed memory (MPI) and shared memory (OpenMP). HPC
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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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(e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative