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position as research assistant for up to 6 months before starting the PhD program. The position is available from 1st September 2025 or soon thereafter. Application deadline is 20. June 2025. Research
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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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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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publications and presentations at international conferences. We are looking for a candidate who: Think creatively and approaches problems with an open, computational mindset. Is motivated to bring data-driven
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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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: 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 The assessment of the applicants will be made by Dr. Lei Yang and Prof. Johannes Kabisch (Norwegian University of Science
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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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. 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