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across both surface and subsurface layers. This includes constructing robust feature extraction pipelines, attention-based fusion architectures, and deep learning models that accurately characterize cracks
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Set up and run simulations to increase our understanding about the factors that influence the change in genetic variance components in populations under selection. Support the postdoc with developing and
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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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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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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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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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, including abstract geospatial workflows; design AI- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute