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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design a public transport network that includes both traditional fixed lines and flexible on-demand services, while
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Machine Learning, and has over 30 PhD students, postdoctoral researchers and faculty members working on a broad variety of deep learning, computer vision, and foundation model subjects, like self-supervised
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with colleagues with diverse backgrounds? The Informatics Institute is looking for an ambitious PhD student on perceptual foundation models. Your research is part of the Video & Image Sense Lab. Join our
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decision support tools. These mathematical models help battery owners make strategic decisions, such as when to charge and discharge or on which electricity markets to focus. In this PhD trajectory, we
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batteries enter the power system, requires state-of-the-art decision support tools. These mathematical models help battery owners make strategic decisions, such as when to charge and discharge or on which
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“Mathematical Foundations for Explainable AI”, funded by the Dutch research council (NWO). You will be part of a team with another PhD student and two consecutive postdoctoral researchers, supervised by Dr. Tim
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University. The project will be supervised by Dr. Koen Haak and involve collaborating closely with a postdoctoral researcher, the group of Prof. Frans Cornelissen at UMCG, as well as with the multidisciplinary
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the research project “Mathematical Foundations for Explainable AI”, funded by the Dutch research council (NWO). You will be part of a team with another PhD student and two consecutive postdoctoral researchers
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available projects . Please indicate in your cover letter which of the following projects you're interested in: PhD 1: A collisional model for dust aggregates, based on continuum mechanics PhD 2: Dust