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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This is NTNU NTNU is a broad-based university
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
(Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
by the Research Council of Norway and the partners for the period 2026-2030. There will be approximately 25 doctoral scholarships and some postdoc positions. Duties of the position Complete
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» Maritime engineering Engineering » Computer engineering Computer science Architecture » Naval architecture Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 23 Aug
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) Karima El Ganaoui, ESTI Pau CY Tech, M24, duration: 4 weeks, AI materials Duties of the position Take part in the mandatory PhD research education programme Develop a quantitative deep-learning-based
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to some extent: A programme combining measure development with field or team studies is plausible after discussion with the supervisory team about samples, settings, methods and analysis. Your work
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The students will be enrolled in the structured PhD programme of the Medical School, NKUA. About the project/work tasks: This position is part of: 19 PhD Fellowships available in Digital Endocrinology in
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outputs under the host doctoral programme and EMANAIRE plan. Take part in local and network-wide training, cohort activities, reviews, dissemination and practitioner engagement. Undertake the six-month
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. Complete a doctoral thesis and related research outputs under the host doctoral programme and EMANAIRE plan. Take part in local and network-wide training, cohort activities, reviews, dissemination and
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and