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23rd September 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Efficient Edge Intelligence Models Apply for this job See advertisement
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of the PhD regulations for more information. You must have a master's degree or equivalent in computer science, artificial intelligence, machine learning, computer vision, or signal processing. The degree
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Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status
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Working capital that can be used to implement the project Mentor programme as a new employee at NTNU Favorable terms as a member of the Norwegian Public Service Pension Fund (SPK) As a PhD Candidate at NTNU, you
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to the requirements of the position. Required selection criteria You must meet the requirements for admission to the faculty's Doctoral Programme , see Section 6-1 of the PhD regulations for more information. You must
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feedback modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. Description of the SPARK4B+ project The position is within the EU
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science » Cybernetics Computer science » Informatics Engineering » Electrical engineering Engineering » Electronic engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline
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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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, mechatronics, control systems, computer science or equivalent, with strong training in robotics, computer vision, state estimation, machine learning or statistical signal processing Your course of study must
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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