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architecture exploration, hardware/software co-design and operating/runtime systems. Typical application domains are e.g. signal-/image processing, artificial intelligence and machine learning. Tasks: research
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for immunological platforms such as flow cytometry, single-cell transcriptomics, spatial transcriptomics, metabolomics and proteomics, imaging, and in vivo work. Numerous interactions with other groups working in
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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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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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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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24.06.2021, Wissenschaftliches Personal The Professorship of Data Science in Earth Observation (Prof. Xiaoxiang Zhu) is seeking a research associate in the market of AI for Earth observation in
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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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in static and dynamic 3D reconstruction, semantic scene understanding, and generative models for photo-realistic image/video synthesis. Overall, the main focus is on high-impact research with the aim