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laboratory-scale turbines. Combined with modern measurement methods, these facilities offer ideal conditions for addressing real-world wind energy challenges under controlled laboratory settings. The position
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, remanufacturing, repurposing and recycling to each other for the realization of an agile network. Various machine learning approaches will be used here. Your tasks are: Requirements definition, survey and
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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positions (TV-L E13). Addressing global challenges, the school provides a wide variety of topics, from logic in autonomous cyber-physical systems to machine learning in Earth System models. You will have one
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areas: software testing, runtime monitoring or diagnostics combined with a desire to embed these in today's assurance processes. Knowledge of software development methods (especially agile methods such as
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areas: software testing, runtime monitoring or diagnostics combined with a desire to embed these in today's assurance processes. Knowledge of software development methods (especially agile methods such as
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to fluctuating inflow conditions specific for new generations of large offshore wind turbines and wind farms. Providing system services opens new degrees of freedom, which can be used to optimise