23 electrical-power-engineering PhD positions at NTNU Norwegian University of Science and Technology
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4 Jun 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Manufacturing and Civil Engineering Research Field Engineering Researcher
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24 May 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Materials Science and Engineering Research Field Technology » Materials
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automation engineering, computer engineering, electric power systems, simulation and visualization. Our research areas include autonomous vessels, robotics, cybernetics, medical technology and health
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16 May 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Marine Technology Research Field Engineering Researcher Profile First
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). If you are a student at NTNU starting in the last year of the master education in study programs related to engineering cybernetics at the Faculty of Information Technology and Electrical Engineering, you
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studies». Magnetic skyrmions are nanoscale stable magnetic textures which move when a current is applied. Due to their potential for low-power operations, they are contenders for the next step in low-power
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2 May 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of energy- and process engineering Research Field Environmental science
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8 Apr 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Materials Science and Engineering Research Field Physics » Solid state
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3 Jun 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Geography Research Field Geography Researcher Profile First Stage
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(or equivalent) within either Electrical Power Engineering or computer science with a specialization in one or more topics such as informatics, flexibility markets, congestion management, machine learning