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Field
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, intelligent vehicles / robotics, acoustics and signal processing, computer vision. Demonstratable experience in applying Deep Learning, using PyTorch, TensorFlow, JAX on real-world sensor data. Experience with
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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systems (including robotics perception/planning stacks). Mentoring: Supervise MS/PhD students; co-author high-impact publications. Service: Support program development, workshops/symposia, and departmental
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systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural language understanding (to interpret instructions), and action generation (to respond), enabling robots
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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experience with CAD/FEM software Experience in one or more of the following fields is a plus: simulation frameworks (e.g. SOFA, NVIDIA IsaacLab), ROS1/2, machine learning, computer vision Excellent
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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Offer Description We are seeking a PhD student to develop the project ‘MultiLearn: A multimodal approach to language learning: a window into the linguistic skills of children with neurodevelopmental
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A three-year PhD position in computational condensed matter physics is