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Field
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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situated knowledge on corridor potentials for housing, to experiential forms of learning based on embodied experiments with alternative social practices. The conceptual and methodological approach is to
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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral
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theory and experiment. By integrating quantum optics, quantum information theory, and machine learning, the project seeks to establish scalable hybrid spin–orbit quantum links. Free-space connections
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of extensive datasets. You will be supervised researchers who collectively offer expertise in computational biology, genetics, epidemiology, and machine learning. The research will be closely linked
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the upcoming flood type, e.g. heavy-rainfall flood or rain-on-snow flood. As PhD candidate you will compare several machine-learning based algorithms regarding their ability to predict the flood type based
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cells survive treatment and cause the tumor to relapse. Furthermore, these therapies lead to long-term problems for children, including difficulties with memory, learning, and daily activities. Hence
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, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en
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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar
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Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH | Bremen, Bremen | Germany | 3 months ago
grants. Research areas of BIPS (and faculty members) include pharmacoepidemiology and cancer screening (Ulrike Haug), prevention and implementation science (Hajo Zeeb, Daniela Fuhr), biostatistics, machine