41 bayesian-inference-tracking positions at Technical University of Munich in Germany
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knowledge in Machine/Deep Learning with experience in discriminative models, domain adaptation, and variational inference. Excellent analytical, technical, and problem solving skills Excellent programming
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and
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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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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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disciplines (typically mathematics, physics). For Postdocapplicants: Excellent track recordin computer science or engineering. Fluency in spoken and written English is required. Proficient in at least one
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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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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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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