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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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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
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quality, and real-time AI performance. This research hub, tackles the intricate challenges of cyber-disturbances and data quality in Edge Computing (EC) environments supporting AI algorithms. The role
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filtering, target detection, tracking and classification. Evaluation of algorithm performance across environmental conditions is needed to quantify uncertainty and allow confidence in decision making. Sensor
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collaboration between the OU and Teledyne e2v (T-e2v), a world-leading manufacturer of scientific and industrial image sensors. The CEI is dedicated to conducting research into advanced imaging technologies
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of fingers, the shapes 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
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-identified scans, records and sensor feeds to answer questions such as: Can we predict a patient’s response to treatment without ever seeing their raw file? Can an algorithm learn the warning signs of trouble
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transmission methods (wired or wireless) will be optimised for robust data capture in natural sleep environments. AI-Driven Analysis: Develop advanced AI algorithms to analyse the collected sensor data, aiming
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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 from in-service