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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques
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Description Primary Duties & Responsibilities: Lead the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and
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the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and hardware accelerators. Interact and collaborate
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for innovative scientific discoveries. The ALS is a global leader in soft x-ray science and is undergoing ALS-U, a large-scale upgrade project to a fourth-generation light source. This position can be hired
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computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
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) general-purpose hardware such as accelerators for AI and ML, high-performance computing, low-power edge computing, quantum computing, cybersecurity, chiplets, and CPU, TPU, GPU, and FPGA systems; or (2
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-efficient designs, GPUs and HPC), Data Science/AI/Machine Learning (e.g., fundamentals, trust and explainability, LLMs, autonomous systems, computer vision), Security (e.g., fundamentals, hardware/software
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dynamic research environments, exhaustive training opportunities and institutional collaborations. The PhD candidate will benefit from the computational resources available at CEPAM (GPU servers). He/She