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modern high performance computation facilities and parallel computing clusters (CPU and GPU). Excellent publication record and demonstrated conference presentation skills. Demonstrated ability to operate
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multiphase flows Your tasks Develop and extend the in-house GPU-accelerated multiphase Lattice Boltzmann (LBM) code for DNS-grade boiling multiphase flow related to nuclear reactor operation, including bubble
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the supervision of one or more of its members, in one of the following projects: - Fundamental physics: the ESPRESSO road to ANDES - Dark Energy, From Alpha to Omega - Coding the Cosmos in the GPU Era: Do
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inhibitors with improved efficacy The project offers a highly interdisciplinary research environment spanning computational chemistry, cell biology, physics, and materials science. The work will leverage GPU
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biology results The project offers a highly interdisciplinary research environment spanning computational chemistry, neuroscience, molecular biology, and psychology. The work will leverage GPU computing
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required) Experience with machine learning / deep learning (PyTorch; model training; GPU workflows). Experience with Transformers / text embeddings / multimodal modeling (e.g., Hugging Face ecosystem
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skills (Python preferred), with familiarity in GPU or distributed computing environments. • Experience with biomedical or neuroimaging data is advantageous but not required. • Excellent analytical, writing
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modern high performance computation facilities and parallel computing clusters (CPU and GPU). Excellent publication record and demonstrated conference presentation skills. Demonstrated ability to operate
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communicate results clearly in writing and presentations. Desired Qualifications: Knowledge of GPU architecture and GPU programming. Interest or experience in distributed training on large scientific datasets
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development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D