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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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the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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biological catalysts — under operational conditions. Your Responsibilities: Develop and implement in situ EPR methodologies on state-of-the-art spectrometers Conduct research at the intersection
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-time data transmission and processing, ensuring efficient and accurate monitoring of storage site conditions. Additionally, you will be responsible for creating and refining a real-time demonstration
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Implementation, further development and adaptation of AI models and research prototypes for the annotation and scoring of text data, in particular for the optimization of AI-supported teaching and learning in
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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to an unprecedented extent. The main topics include the description and modelling of wind turbulence, the analysis of interactions of turbulent atmospheric wind flow and wind energy systems, as well as control of wind