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Field
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research of Mechanical Engineering, Civil Engineering and Industrial Design Engineering. We enable society and industry to innovate and create value using efficient, solid and sustainable technology. We
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of Mechanical Engineering, Civil Engineering and Industrial Design Engineering. We enable society and industry to innovate and create value using efficient, solid and sustainable technology. We are part of a
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work with fish behavior, engineering geology, turbine design, variable speed operation, etc. It involves fundamental research including a literature study and CFD modelling with the use of real case
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more sustainable? If you share our passion for technology and the difference it can make in meeting the UN’s Sustainable Development Goals, perhaps you are one of our two new PhD students. At DTU Electro
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
based within the Manufacturing, Materials and Design theme at the Centre for Digital and Design Engineering (CDDE), which offers access to advanced simulation, visualisation, and high-performance
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Profile: Excellent master’s degree in Process Engineering, Chemical Engineering, Mechanical Engineering or a similar discipline Strong interest in pursuing research on electrochemical systems Experience in
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The Department of Civil and Mechanical Engineering of the Technical University of Denmark (DTU) has an open PhD position (3 years) on the topic of “Automation of tool wear measurement and data
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Position in Synthetic Biology and Metabolic Engineering (m/w/d) to be filled as soon as possible. The position is initially limited to three years. The research group headed by Prof. Dr. Hannes Link
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, Water Quality, Water Resources Management, Agriculture, Remote Sensing, Electronics, Artificial Intelligence, Computer Science, Civil Engineering (Structure and Geotechnique), Mechanical Engineering, and
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable