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energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes
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Enthusiasm for an exciting new computing paradigm involving the development of innovative solutions Openness to communicate, cooperate and exchange ideas within a joint endeavor of multiple vibrant research
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pathways for the required paradigm shift to sustainability. This position focuses on the initial work package in the project, to conduct statistical topic modelling on policy documents, ideally across
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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validate designs of microfluidic devices even before the first prototype is fabricated. In this field, we are about to start a consortial project with stakeholders from academia and industry to establish