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automatic interpretation of measurement data. A cornerstone of our systems is the automated, AI-driven interpretation of 3D point cloud data, which requires high-quality training datasets and, ideally, model
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for downstream applications such as mapping, simulation, analyses and other related uses. This PhD investigates methods to transform raw 3D data into structured scene representations that integrate
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novel algorithms for efficient, high-fidelity 3D scene reconstruction and representation, including applications in embodied environments where an agent must continuously perceive, update, and reason
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candidate will develop practical learning-based systems for generative 3D/4D scene modelling, world models, and embodied intelligence. Key Responsibilities: Develop and implement machine-learning methods
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analysis in the construction domain/synthetic data creation and transformation. The group develops AI methods for 3D model creation, plan and scene analysis in the construction sector by analyzing as-built
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Job description Next generation automotive imaging radars provide increasingly rich 3D information, opening the door to more advanced scene understanding and perception tasks such as 3D object
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scene, and leveraging that understanding for localization and navigation. A robot builds a picture of its surroundings one lidar scan at a time. Each scan on its own says very little; together
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centralized intelligence for mission-critical situational awareness in dynamic environments. You will build and maintain real-time semantic 3D scene representations across heterogeneous sensors and robots
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grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Key Responsibilities: Conduct research on 3D/4D scene
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on advanced perception and scene understanding for robotic harvesting. The successful candidate will contribute to the development of novel methods for: simultaneous 3D scene reconstruction and plant modelling