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Computing Applications and Research (CHEDDAR), we are focused on advancing the integration of UK expertise in communications and networking to maintain the country's leadership in 6G technologies. Our
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The emergence of edge AI systems—AI deployed on resource-constrained, often battery-powered, devices at the edge of the network—presents critical security challenges. These systems are increasingly
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of tensor networks to quantum and classical reservoir computing Additionally, the candidate will have the opportunity to collaborate with experimental groups at the Jožef Stefan Institute (IJS) working
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. This interdisciplinary collaboration with the University of Lincoln and University of Liverpool focuses on developing innovative computational approaches that integrate advanced genomic analysis with state-of-the-art
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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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computer science, engineering, mathematics or physical sciences area Recent high quality research experience in machine learning/AI, or both, as evidenced by a strong track record of publications in leading
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Australian National University | Canberra, Australian Capital Territory | Australia | about 18 hours ago
university Advance transdisciplinary scholarship through research, collaboration and high-impact practice Engage with regional communities in the development of community-led flood intelligence networks
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. Creating a world where we all have more time with the people we love. That’s what makes us Genentech. Are you a highly passionate computational biologist looking to make a significant impact in cancer
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. • Join journal clubs. • Conduct a wide range of neural network analysis Job Requirements The project requires a researcher with a • PhD in Computer Science, Biostatistics, Statistics or other related
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methodologies, and publish at a high level. The lab offers deep integration between wet-lab and computational biology, and close connections to institutional resources and collaborative networks across Duke-NUS