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aims at addressing computational challenges associated with data acquisition and information extraction from complex sensors and sensor networks. Crucially, uncertainty management and quantification
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project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours
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systems with a particular emphasis on methods and systems that cope with imperfect knowledge and uncertain sensors. The research environment provides excellent opportunities for open-minded co-operation
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of the fingers, and the positions of tactile sensors), and the control policy for that hand, when given a particular task or set of tasks. Through this, we aim to develop a framework that can automatically
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-time systems. The role will involve working with large and multi-modal datasets (e.g., images, video, audio, and sensor data), and deploying solutions in real-world environments, particularly in robotics
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, audio, and sensor data), and deploying solutions in real-world environments, particularly in robotics-focused applications. Familiarity with robotics concepts such as SLAM, sensor fusion, visual odometry