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Field
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/ is a future-oriented research group with the main strengths and focus topics: Self-driving vehicles, driving algorithms and cyber-physical system Sensor fusion, perception and big data Cybersecurity
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Change: Leverage high-resolution tactile sensors to handle contacts Impact: Autonomous grasping and manipulation Job description Dexterous manipulation represents one of robotics' most fundamental unsolved
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges
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Challenge: Control robotic dexterous hand during dynamic movements Change: Leverage high-resolution tactile sensors to handle contacts Impact: Autonomous grasping and manipulation Job description
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vessels and ship systems. Knowledge Graphs based on engine propeller combinator diagrams of the same vessels. Machine learning algorithms for data clustering and regressions of ship performance and
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Analyze multi-sensor data to evaluate system performance Design, implement, integrate, and test software to demonstrate guidance and control algorithms Collaborate and communicate with a team of peers
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-house database of experimental real-world data enabling large-scale validation of developed algorithms. Wind turbine drivetrains are critical components, and their failures can lead to significant
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increasing environmental awareness. However, most existing hearing aid algorithms optimize for only one of these objectives at a time, often at the expense of the others. To enhance hearing aid performance
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quantum sensing technologies (e.g., Rydberg atomic sensors) for wireless communications and sensing. Key Responsibilities: Develop quantum-related theories, models, and algorithms for various communications