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on developing new ML approaches to challenging quantum problems, with a strong focus on making these approaches interpretable, reliable, and scalable. In this position, you will learn about state-of-the-art deep
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(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT
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of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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Humanities and Law are organized into different Units: Entrepreneurship, Ethics and Leadership Governance, Culture and Learning CBS Law Faculty within these units have research backgrounds in various areas
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to frailty assessment could be beneficial. Manual measurements from CT scans, however, are labor-intensive and subject to observer variability. The advent of deep learning in medical imaging presents a
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for Sustainable Energy, researchers from academia and industry develop, implement and evaluate new deep reinforcement learning methodology to solve sustainable energy challenges. Key responsibilities The lab is
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The successful applicant will conduct research to design and develop novel machine/deep learning based trust technologies for securing IoT services/devices. The successful applicant will conduct
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry