91 parallel "Computer Vision Center" "Computer Vision Center" PhD positions at Technical University of Denmark
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intersection of computer vision, AI, and marine biology. This project aims at developing AI-based methods to transform underwater imagery from a simple visual record into a quantitative tool to measure species
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Job Description Are you passionate about computer vision, deep learning, and multimodal AI, and excited to shape how humans and machines communicate? Do you want to help build the next generation of
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interact with the three-dimensional world. We develop methods in computer vision, machine learning, and multimodal representation learning that enable autonomous systems to reason about objects, people
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Job Description Are you excited about scientific AI and developing efficient computer vision methods for analysis of complex 3D scans of natural heritage samples? Join our interdisciplinary team as a
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assistant professor or associate professor whose research advances parallel and high-performance computing. Relevant areas include computer architecture, operating systems, compilers and the
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. Publish research results in leading journals and conferences in machine learning, computer vision, and computational imaging. Contribute to research-based teaching and supervision of MSc and BSc student
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DTU Compute, and embedded in the Section for Visual Computing. The Section for Visual Computing is focused on medical image analysis, geometry and appearance modelling, computer vision, multivariate
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candidates for the position. The Center for Visualizing Catalytic Processes (VISION) is funded by the Danish National Research Foundation and located at DTU Physics. VISION is developing and applying
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images of natural heritage specimens from the robotic CT system at the DTU 3D Imaging Center. Your primary activities include: Develop new mathematical methods for fast reconstruction of CT data from fast
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at the Center of Energy Resources Engineering, DTU Chemical Engineering. You will be involved in the development, parameterization and validation of new coarse-grained molecular models and the