34 phd-electrical-power-engineering PhD positions at NTNU Norwegian University of Science and Technology
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as a PhD Candidate, the goal is a completed doctoral education up to an obtained doctoral degree. This project aims to demonstrate a proof-of-concept multilayer device for efficient low-power readout
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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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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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degree. We have a vacancy for a PhD Candidate within wind and digital twin areas at the Department of Marine Technology . We are seeking a highly motivated PhD candidate to join our team in the project
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16 May 2024 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Clinical and Molecular Medicine Research Field Medical sciences
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Ecology Program (IndEcol), Department of Energy and Process Engineering (EPT) at the Norwegian University of Science and Technology (NTNU) has an opening for a PhD position on Climate Change Mitigation in
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least one of the mandatory course work from the PhD program in Cybernetics, see https://www.ntnu.edu/studies/phtk/coursework For applicants with primary background in control engineering: Ability and
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to 120 credits) with a strong academic background in photonics, materials science, physics, electrical engineering, or nanotechnology (and other revelant disciplines) with focus on photonics. You must have
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the job A PhD Candidate position in the field of molecular-based ferroelectric materials development for electrical energy storage capacitors is available. The academic position will provide
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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