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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint
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approach that will be used is Challenge-Based Learning (CBL) in which multi-disciplinary teams of students learn by conducting research and design projects on a societal problem in collaboration with
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differences in learning, memory, and processing between these systems. This project develops the necessary methods to study how smart AI-models are compared to people, now and in the future, and sheds light on
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is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within
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about everyone’s research project and try to help and learn from each other’s problems to boost our scientific and personal growth. We also enjoy many team-building activities and events where you will
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University of Technology (TU/e). Our group consists of six full professors, three associate professors, seven assistant professors, several postdocs, approximately 40 EngD and PhD candidates, and support staff
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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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- 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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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