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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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of Norway (RCN) and are supported by the Centre of Excellence funding Scheme by the RCN (the Centre for Algorithms in the Cortex), as well as the Kavli Foundation. The Zong group is further supported through
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are differentially private algorithms for statistical model parameter estimation under different trust relations. About the project The position is funded by the Norwegian Research Center for AI Innovation and will be
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analytical skills, analyze data, assess different perspectives and draw well-founded conclusions Strong motivation to contribute to a good working and social environment In the evaluation of which candidate is
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, result-oriented Excellent analytical skills, analyze data, assess different perspectives and draw well-founded conclusions Strong motivation to contribute to a good working and social environment In
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analytical skills, analyze data, assess different perspectives and draw well-founded conclusions Strong motivation to contribute to a good working and social environment In the evaluation of which candidate is
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to work independently as well as in teams Work in a structured way, set goals and make plans to achieve them, result-oriented Excellent analytical skills, analyze data, assess different perspectives and
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analytical skills, analyze data, assess different perspectives and draw well-founded conclusions Strong motivation to contribute to a good working and social environment In the evaluation of which candidate is
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analytical skills, analyze data, assess different perspectives and draw well-founded conclusions Strong motivation to contribute to a good working and social environment In the evaluation of which candidate is