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, and supportive atmosphere, equipped with state-of-the-art research facilities, including dedicated GPU clusters, data servers, and personal GPU-enabled workstations. You will join a multidisciplinary
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/GPUs. These devices provide massive spatial parallelism and are well-suited for dataflow programming paradigms. However, optimizing and porting code efficiently to these architectures remains a key
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optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus. Demonstrated research potential is highly desirable
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technological platforms on site. The PhD student's work will be carried out at the IGBMC's integrative biology center. He/she will have privileged access to the team's computing server (GPU node) and the
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scientific computing. You are proficient in several languages (Python, C/C++, or Fortran), with extensive knowledge in AI/ML and parallel programming (GPU, multi-threading, etc.). You have strong software
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degree in the above mentioned or related fields. What we offer State of the art on-site high performance/GPU compute facilities A team of 30+ expert colleagues A family friendly, green campus with on-site
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artificielle (IA) (CPU, GPU, accélérateurs d'IA, etc.) nécessitent une puissance élevée et des réseaux de distribution d'énergie (PDN) optimisés pour améliorer l'efficacité en puissance et préserver son
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on materials science tasks as well as integrate your semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with
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different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research articles, regular participation in top international conferences to present your work
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, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs) to improve power efficiency and preserve power integrity. Integrated voltage regulators (IVRs