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causing these variations in A and F stars. Using asteroseismology, we aim to detect and analyze near-core and surface magnetic fields. This involves comparing theoretical models with photometric
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of meteorological phenomena impacting the wind energy sector, in particular with respect to wind resources and wind conditions. For example, investigating how large-scale wind farm clusters may impact atmospheric and
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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potential for exploiting temperature gradients for producing electricity and predict their long-term performance under real operating conditions. The project also includes modeling of heat transfer and
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the nanoparticles will dissolve and release their nutrients. Field tests under controlled conditions will be conducted in collaboration with the other partners involved in the project. At DTU Energy, you will have
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simulate wind and solar forecast uncertainties on pan-European level, leveraging latest machine learning weather forecast models Apply machine learning methods to forecast day-ahead and balancing market