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Field
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machining offers a flexible alternative where industrial robots hold the cutting tools. However, these systems lack the real-time perception and intelligence needed to detect stresses, predict tool wear, and
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. Long-horizon and safety-aware planning under uncertainty. Function 2 2. Learning-Based Perception and Control Multi-modal and foundation-model-based approaches for robotics, including: Multi-modal
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experience in the following areas: Applied Machine Learning for Autonomous Systems: Experience developing and deploying ML models for perception, prediction, or decision-making in autonomous driving or robotic
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» OtherEducation LevelPhD or equivalent Skills/Qualifications Who you are PhD in Computer Science, Machine Learning or related field or 5+ years of relevant industrial experience. Strong track record of developing
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foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands
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help the team with the Computer Vision aspects of the project. Position Overview: As a doctoral researcher, you will forge a novel framework for cooperative perception by disrupting today’s most vibrant
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Machine Learning Group, Department of Engineering, CambridgeMLG Cambridge About Us News Research Publications People PhD Admissions Blog Latest News Papers with MLG authors to appear at ICML and
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behavior. • Year 2: investigation of modeling strategies for early perception, including spiking architectures and attention-inspired mechanisms; comparison of different learning paradigms and temporal
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is demonstrated on real robotic systems like cars AGVs and UAVs together with industrial partners. 1 Context Modern robotic systems increasingly rely on learning-based perception, prediction, and
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Center for Project-Based Learning. The successful candidate will contribute to research at the intersection of embedded machine learning, signal processing, and smart sensing systems, with applications in