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. The ideal candidate will have: • A Ph.D. in urban studies, geography, architecture, or a related field. • Extensive experience in ethnographic and qualitative research methods. • A proven track record of
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well as knowledge representation and inference. In the research project DrawOn, new technologies for analyzing 2D digital drawings and reconstructing 3D building models will be developed. The goal of this project is
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Debugging: Developing mechanisms for synchronized state tracking across multi-core/multithread RISC-V architectures, ensuring consistent register states, memory coherence, and excep-tion handling during
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to revolutionize the research field in 3D learning. Research topics include: - Neural Rendering: 3DGS, NeRF, etc. - Generative AI: Diffusion, LLMs, GANs, etc. - 3D Reconstruction - SLAM / Pose Tracking (SfM, MVS
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candidate will show in-depth methodological and applied knowledge in the field of machine learning, especially deep learning, experiences in the area of uncertainty quantification, generative and Bayesian
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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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emphasis is placed on building information modelling, point cloud capturing and processing as well as knowledge representation and inference. In the research project AI-CHECK, new technologies for checking
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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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motivated PhD students to strengthen our interactive and collaborative team. The projects are founded on the well-established and highly visible track record of the laboratory in the analysis of plant growth