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learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute to advancing efficient AI systems. Are you motivated to take a step
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available1Company/InstituteDepartment of Computer ScienceCountryNorwayCityTrondheimPostal Code7491StreetHøgskoleringen 1Geofield Contact City Trondheim Website http://www.ntnu.no Street Høgskoleringen 1 Postal Code
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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Technology » Computer technology Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Oct 2026 - 23:59 (Europe/Oslo) Country
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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, and at NTNU we aim to be an employer that reflects the diversity in society and that makes use of the potential of the population's collective skills. Our vision is Knowledge for a better world and our
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that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program(https://www.ntnu.edu/studies/phiv ) Very good English skills (both
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robotics or computer vision or machine learning. Your education must correspond to a five-year Norwegian degree program, where 120 credits are obtained at master's level. You must have a strong academic
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are applied in real-world settings characterized by large-scale networks, stochastic demand, operational disruptions, and complex constraints. A central research question is how machine learning can be
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team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation