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Mechanical, Production, Materials, Industrial or Robotics Engineering, Computer Science, or related discipline, or a similar degree with an equivalent academic level. Approval and Enrolment The scholarship
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in machine learning and artificial intelligence Experience with numerical analysis and scientific computing Knowledge of power systems and renewable energy technologies Experience in power system
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: Participate in an international research exchange program (3 months to 1 year) at a partner university or research institute to gain new skills and enhance collaboration. Qualifications A master’s degree (or
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of parasitic in electric machine Experimental Work: Develop, demonstrate and test the electric drive system International Exchange: Participate in an international research exchange program (3 months to 1 year
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of the PhD study programme, please see DTU's rules for the PhD education . Assessment We will be using shortlisting based on educational background, experience and preferred qualifications and call a number of
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, Computer Science, or related discipline, or a similar degree with an equivalent academic level. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate
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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Prof
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of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Dr. Lei Yang and Prof. Johannes Kabisch (Norwegian University of Science
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influence its development directly. We are currently evolving our mechanical engineering degree program and constructing labs to support both educational activities and research. Workplace description
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. The overarching goal of this newly funded project is to realize quantum light sources coupled to quantum memories. Quantum memories are key components of optical quantum computers and scalable quantum networks