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a strong background in artificial intelligence and 3D computer vision, with solid programming skills and an interest in 3D data processing (e.g. point clouds and neural scene representations), along
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, computer vision or machine learning or Documented Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering, scaffolds
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models for high tech industry applications. Your results are expected to be published at leading international venues in machine learning, computer vision, robotics and radar, such as NeurIPS, CVPR, ICRA
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involving robotics, computer vision, SLAM, mobile sensing, sensor fusion, or related areas will be valued. Criterion a3. Technical and programming skills (30%). Strong proficiency in Python or C++, and
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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vision, robotics and radar, such as NeurIPS, CVPR, ICRA, IEEE IV and RadarConf. For your research, you will have access to extensive computing resources at TU Delft, ranging from personal workstations and
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processing, computer vision or machine learning or Documented Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering
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understanding with language-based reasoning. Process micro-facial expression data more efficiently in computer vision and vision language models. Create a language-guided representation for subtle facial motion
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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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, relating to craniofacial identification research and 3D digital avatars. You will require a 3D animation, CGI, computer science and/or anatomical modelling background. A knowledge of anatomy and 3D