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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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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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Trondheim, Norway, but the Researcher-project is of both academic and industrial relevance, and it will therefore be developed in close cooperation between NTNU in Trondheim and the company Petronas, which is
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”. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions and Regulations for the degrees philosophiae doctor (ph.d.) and philosophiae doctor (ph.d.) in artistic development work
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for the degrees philosophiae doctor (ph.d.) and philosophiae doctor (ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred
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actors in Norway and be in a unique position to develop an attractive career either in industry or in research/Academia. Your immediate leader will be the Head of unit, Applied Information
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24th October 2025 Languages English English English The Department of Computer Science (IDI) has a vacancy for a PhD Candidate in Information Infrastructure Development of Offshore Energy Hubs
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supply and a path to a net-zero-emissions society. SecurEL aims to develop new knowledge addressing research challenges arising from accelerated electrification and increased use of the existing grid
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philosophiae doctor (ph.d.) and philosophiae doctor (ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection