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fundamental AI methods together with their software implementations for interpretable statistical fault prediction and lifetime assessment in the context of Structural Health Monitoring of operating wind
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to NLP, KGs, and Digital Cultural Heritage research. Stipend 1: Applicants for the position in the Department of Computer Science must have: • A relevant MSc degree (e.g., Computer Science, Software
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Ariya Sangwongwanich from AAU Energy. The prospective Ph.D. student will be co-supervised by Associate Professor Hongbo Zhao (AAU Energy) and collaborate with other Ph.D. students and Postdocs in the host
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M. Smedskjær. The prospective Ph.D. student will be co-supervised by Tenure Track Assistant Professor Søren S. Sørensen and collaborate with other Ph.D. students and Postdocs in the research group
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into applications in close collaboration with industrial partners worldwide. You can read more about the department at www.es.aau.dk. Your work tasks Modern wind turbine testing and verification face a series of data
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of student projects and participation in courses related to human-computer interaction and software engineering. Your competencies Applicants should have a strong interest in human-robot interaction and the
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, software, and High Performance Computation (HPC) infrastructure; • Excellent scientific infrastructure; • Participation in project meetings and international conferences; • Flexible working hours
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project is in close collaboration with the Head of Software - Technology and Innovation at EBU. You will have an extended and paid stay in Geneva with the EBU. As part of RePIM you will participate in
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Python and/or Matlab. Experience with software-defined communication systems, emulation tools, and machine learning techniques is considered an advantage. Most importantly, you are eager to learn and apply
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Experience with firm level econometric analyses or with processing of large firm level datasets will be an advantage, but it is more important that you have experience with software tools such as Stata, SAS, R