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to maximize 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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an overall picture of future mobility together with the project partners. Your Profile You have a strong interest in automated driving and mobility transition. You have completed your master's degree in a
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channels, going beyond the Shannon paradigm, which offers many exciting open questions to work on. Thirdly, the project aims at investigating non-Shannon-type inequalities for the quantum entropy, which in
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simulators (on various abstraction levels using, e.g., Computational Fluid Dynamics) which enables us to verify designs of microfluidic devices even before the first prototype is fabricated. Fabrication: We
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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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of microfluidic devices even before the first prototype is fabricated. In this field, we are involved in a consortial project with stakeholders from academia and industry to establish those tools for practical
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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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of acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text
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interactions 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