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for learning applications. The fellow will design approaches that ensure high data utility while rigorously mitigating risks of re-identification and privacy leakage. The work from the PhD fellow will
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experience with qualitative research methods, such as interviews and/or design-based research be able to work independently and in a structured manner, and have good collaboration skills possess excellent
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, and validating methods for synthetic data generation that enable secure and trustworthy data sharing and exchange for learning applications. The fellow will design approaches that ensure high data
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of maritime business models. As digital solutions replace manual coordination and increase data-driven transparency, the patterns of supplier relationships, contractual arrangements, and responsibility
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digital solutions replace manual coordination and increase data-driven transparency, the patterns of supplier relationships, contractual arrangements, and responsibility boundaries are likely to change
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disciplines involved in the development of film, TV, and games, such as producing, fiction directing, documentary directing, scriptwriting, cinematography, production design, editing, sound design, film music
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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar
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PhD candidate in AI-driven ship design, connected to the newly established Norwegian Maritime AI Centre . The position aims to advance how artificial intelligence support ship design processes
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to improve detection capabilities, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning
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of research questions, theoretical perspectives, methodological design for the project and progress plan (maximum 1500 words/4 pages) Short letter of motivation (400 words/1 page) Possibly publications etc