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Field
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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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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consists of three major parts: sensor printing, circuit design and integration and developing of an AI algorithm and using it to teach the sensor to selectively measure desired gases . In this role, you will
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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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Multi-Sensor Data Fusion and Smart Decision Making School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline: Applications
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Interplay between Algorithms and Combinatorics
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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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of multimodal imaging techniques, such as computer vision and hyperspectral imaging, alongside Internet of Things sensors that will continuously monitor critical parameters such as temperature, humidity and food
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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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-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research