48 structures "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" scholarships at NTNU Norwegian University of Science and Technology in Norway
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the integration of AI-based digital systems in new product development (NPD) processes influences organizational dialogue, participation and authority structures in complex innovation systems. The possibilities
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well as researchers from 15 international institutions. More on the project at http://sure-ai.no . Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality
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/ML methods to improve drillability, increase rate of penetration (ROP), reduce non‑productive time, and enable cost‑effective geothermal well construction. The work includes evaluating geothermal
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arising from the inclusion of scrap, and to control the processing parameters such that the structural integrity of the cast components can meet with the demands to performance. The starting date is August
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) Personal characteristics We are looking for someone who: Is curious, structured, and able to work independently on complex research problems Collaborates well in interdisciplinary teams and contributes to a
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affiliated with the Centre, and opportunities for research stays with partners, both nationally and abroad, through its international network. Read more about the Centre and its research at the website: https
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team Work in a structured way, set goals and make plans to achieve them Present and discuss your research with other professionals Get involved and contribute constructively with feedback Show enthusiasm
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), it is important that you are able to: Have a high level of personal responsibility, initiative, motivation and ability to work in a project team as well as independently. Work in a structured way, set
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structured way, set goals and make plans to achieve them Present and discuss your research with other professionals Be highly motivated by fundamental scientific research of practical relevance Get involved
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, and reinforcement learning for adaptive decision-making. A key aim is to connect wireless phenomena to learning robustness by combining physical-layer signal structure and signal-processing insights