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. Job Requirements: Preferably Bachelor’s degree in Computer Science or related disciplines from a reputable university. Good knowledge in parallel and distributed computing, and algorithm design
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partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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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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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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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 5 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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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
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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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distributed computing for EMT simulations. • Experience with software development in Python, C++, or other programming languages. • Familiarity with GPU acceleration of numerical solvers, parallel sparse