123 parallel-programming "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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activities We are looking for candidates who have: A strong background in computer vision and machine learning Experience with deep-learning frameworks such as PyTorch Strong programming and
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computer vision, ideally segmentation and/or quantification of micro-CT data experience developing deep learning-based image analysis methods experience with scientific Python programming experience
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research in leading journals and conferences. Attract competitive research funding in Denmark and Europe. Teach and develop courses in parallel computer systems, parallel programming, and HPC, and supervise
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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
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Technology. The successful candidate will undertake a 4-6 month research stay at Chalmers University of Technology and work closely with a parallel PhD project focusing on upper-limb motor control in
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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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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