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Field
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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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90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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, communication and presentation skills, project management, statistical analysis, leadership skills, and many others. This allows our students to acquire transferable skills that are not routinely communicated in
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people, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based
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-source releases and publications Who we’re looking for Required MSc (or equivalent) in molecular biology, bioinformatics, computational biology, statistics, computer science, or similar Solid programming
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committed candidates from varied backgrounds who are eager to contribute to cutting-edge and interdisciplinary research. Successful applicants will ideally have: A relevant MSc degree in computer science
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9 Mar 2026 Job Information Organisation/Company Université Paris Cité Department Center for Research in Epidemiology and Statistics (CRESS) Research Field Psychological sciences » Psychology
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astrophysics, condensed matter physics and solid state theory, statistical and biological physics, mathematical physics, quantum information theory, and nuclear physics. Course organisation All graduate studies
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understanding of neutron scattering and magnetism; Experience with density functional theory; Experience in statistics and programming. • Manual skills and rigorous attention to detail are essential in this work