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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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., neurolinguistics, speech therapy, psychology), experience with neuroimaging or willingness to learn (i.e., navigated Transcranial Magnetic Stimulation, Direct Electrical Stimulation), and capacity to gather and
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be continued. The PhD candidate will be recommended to teach (0.1 fte) during their fourth year of their appointment. Supervision of MA and MSc thesis related to the PhD project may be suggested one
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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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& Statistics Project description Unmanned Aerial Vehicle (UAV) e.g., drones are increasingly used for equipment anomaly and fault detection. When the drones are employed to take images, the quality of the images
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. Measurement techniques in field applications and in the laboratory. Modeling and simulation skills (batteries, energy systems, electric equivalent circuits). Machine learning, statistical analysis, and other
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(position A), data collection to quantify ecosystem functions and services (position B), advanced statistical data analyses and modelling, and writing manuscripts. You will work here Both positions are funded
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. Measurement techniques in field applications and in the laboratory. Modeling and simulation skills (batteries, energy systems, electric equivalent circuits). Machine learning, statistical analysis, and other
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and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please
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conducting biodiversity research at different levels. MSc/Diplom (DE) in a field related to ecology (e.g. pollination, chemical or molecular ecology). Strong experience with statistical data analyses