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6th March 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Modeling Edge AI Computer Architectures Apply for this job See advertisement
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well as innovative and inquiry-based teaching and learning. The Faculty consists of six departments as well as a Faculty administration. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/296312/phd
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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models with drone imagery using machine learning techniques and data assimilation. The work will involve collaboration with an interdisciplinary team of researchers, engineers, and local stakeholders in a
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(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
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promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral
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Computer science Engineering » Computer engineering Technology » Information technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 10 Feb 2026 - 23:59 (Europe/Oslo
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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