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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
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a professionally relevant background in Information security, electrical engineering, electronic engineering and/or Computer Science. Your education must correspond to a five-year Norwegian degree
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and at Kjeller Campus in Viken. The Department of Mechanical, Electrical and Chemical Engineering (MEK) at the Faculty of Technology, Art and Design ( TKD ) has a vacant PhD Fellowship position in
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and at Kjeller Campus in Viken. The Department of Mechanical, Electrical and Chemical Engineering (MEK) has vacant a PhD fellowship position in sustainable innovation and radical technologies and
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approximately 500 employees at the Department of Electrical Engineering and Computer Science, Department of Structural Engineering and Materials Science, Department of Mathematics and Physics, Department
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for admission to the doctoral program in Faculty of Information Technology and Electrical Engineering. Knowledge of fundamentals of C++ programming. Competence in code optimisations. Knowledge of hardware
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research at the Department of Structural Engineering Conduct research aiming to: Improve the current understanding of stochastic fracture behaviour in aluminium castings Develop predictive stochastic
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27 May 2024 Job Information Organisation/Company University of Stavanger Department Department of Electrical Engineering and Computer Science Research Field Computer science » Other Engineering
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research at the Department of Structural Engineering Conduct research aiming to: Improve the current understanding of stochastic fracture behaviour in aluminium castings Develop predictive stochastic
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internationally.The faculty has about 2,800 students and approximately 500 employees at the Department of Electrical Engineering and Computer Science, Department of Structural Engineering and Materials Science
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at the Department of Structural Engineering Conduct research aiming to: Improve the current understanding of stochastic fracture behaviour in aluminium castings Develop predictive stochastic fracture models for large