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supervised deep learning algorithms for 3D laser data from forests Developing self-supervised deep learning algorithms for 3D laser data from forests Expand for a wider variety of downstream tasks focused
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university, the internationally accredited registrar and classification society DNV, and Cancer Registry of Norway. You will be analyzing and developing algorithms for privacy preserving health registry data
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page to watch video, or click here to open video) About the position The research focus of this position will be on anomaly detection. We mainly aim to develop methods that are applicable for static and
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based in the Cryptology discipline of the Department of Information Security and Communication Technology. The candidate will be analyzing and developing algorithms for privacy preserving health registry
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classical methods, thereby enabling more efficient solutions. Over the past 25 years, rapid advances—especially the demonstration of quantum algorithms that challenge the extended Church-Turing thesis—have
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the fundamental limits of quantum error correction (QEC) while concurrently advancing efficient decoding algorithms for quantum error-correcting codes in the near-term, noisy intermediate-scale quantum (NISQ) era
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for Artificial Intelligence are set to launch in the fall of 2025. These centers will foster interdisciplinary collaboration among researchers to address societal challenges, understand the impact of AI, develop
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in the fall of 2025. These centers will foster interdisciplinary collaboration among researchers to address societal challenges, understand the impact of AI, develop new technologies, and drive
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of AI, develop new technologies, and drive innovation across both the business and public sectors. This initiative is established and financed through the government's significant investment in
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launch in the fall of 2025. These centers will foster interdisciplinary collaboration among researchers to address societal challenges, understand the impact of AI, develop new technologies, and drive