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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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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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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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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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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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field (a PhD is essential for the Research Associate position), experience working with wearable sensor data and digital outcomes; proficient and independent signal processing and coding, statistical and
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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