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Field
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position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Computer Science This Ph.D. project will develop fundamental theory and methods in distributed systems
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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Application Deadline 23 Mar 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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The project: The domain of High Performance Computing (HPC) effectively utilises massively multicore computers that facilitate the distribution of scientific workloads across thousands of compute
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learning frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience inscalable data processing, including the use of parallel computing, cloud platforms,and distributed systems
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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including workload schedulers, storage systems, and distributed compute nodes. Applies analytical methods to evaluate system performance, identify bottlenecks, and implement corrective actions to improve
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 2 days ago
Status Full-time Hours Per Week 35 Offer Starting Date 23 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number Fellow_BI/FCT_Proj2025/i3S
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). 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 AI methods as
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content moderation framework for immersive 3D virtual environments, such as Roblox and Minecraft. The engineer will play a key role in building a parallelized, agent-driven exploration system and