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mobile robotics, you will manage own academic research and administrative activities, adapt existing and develop new methodologies in robotics, design working algorithms from theories, deploy and test
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with MoniRail Ltd and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected
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us to build/learn generative, probabilistic forward models of users and their physical and computational environments. This will involve modelling sensors, developing dynamic models for control and
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the University of Southampton, where fibre optic sensors are being developed to measure strain in real time during the cure process. As part of this exciting project, we are collaborating with our spin-out company
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methods for on-sensor computer vision. Specifically, the job involves developing algorithms for embedded systems that are designed to produce sensing and computation on the image plane, and on understanding
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applications would be particularly beneficial, although not essential. In addition, experience in artificial intelligence, cloud computing, sensor networks and/or data visualisation would also be highly
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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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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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and interoperability, and efficient processing and management of time-series datasets and metadata originating from IoT sources (such as environment sensors and meters) closer to the data provider