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Field
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Criteria A MSc degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field; Strong knowledge of and experienced with statistics, machine learning, and stochastic
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are welcome to apply. A keen interest in language evolution, language change, language learning, human evolution, and communication. Experience in conducting empirical research (e.g., experimental design
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leveraged for this work i.e., unsupervised learning, data clustering including statistical modelling and estimation, noise and heterogeneous information fusion, and multiple-source localization and tracking
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a very good MSc degree (or equivalent) in a social and behavioral science discipline (e.g., Psychology, Cognitive Sciences, Decision Sciences) or a computer science discipline (e.g., Computational
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, data scientific or machine learning background that is keen to work in an interdisciplinary environment and open to collaborating with researchers from other disciplines. The successful candidate will
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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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related (e.g., neurolinguistics, speech therapy, psychology), experience with neuroimaging or willingness to learn (i.e., navigated Transcranial Magnetic Stimulation, Direct Electrical Stimulation), 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