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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
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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
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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
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team Other welcomed qualifications knowledge of signal processing algorithms for images, videos or audio is welcomed good graphic design skills with tools such powerpoint and photoshop is welcomed