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Field
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applications of various sensors and actuators; solid mathematical foundation; priority will be given to those with experience in developing industrial-grade control algorithms, signal processing algorithms
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. vision–language and multimodal models) often perform well during training but degrade after deployment due to changes in data, environment, sensors, or user behaviour. The goal of this PhD is to design
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, algorithms must respond in real time and use partial sensor data of varying quality. In addition, I made the decision to focus only on embedded systems, depriving robots of internet connectivity and the myriad
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focuses of this role are in the areas of acoustics, signal processing, algorithm development, modeling, data analysis, and software engineering with an interest in undersea systems. You will join a team of
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There is growing UK and international interest in networked sensing and autonomous collaborative platforms, where multiple airborne sensors co-operate to collect and exploit data. In contrast
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integrating artificial intelligence, from algorithm design to on-sky demonstration. The objective is to design intelligent adaptive optics systems capable of correcting sensor nonlinearities, anticipating
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devices—such as wearable sensors, assistive robotics, or implantable systems—where real-time performance, energy efficiency, and reliability are critical. Unlike traditional NAS approaches that are hardware
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are met. Undertake these responsibilities in the project: Develop and deploy multimodal AI algorithms for fire, smoke, and hot-work detection by fusing optical, thermal/infrared, LiDAR, RADAR, and gas
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selected sensor-based monitoring solutions, establishing data pipelines and data management structures, and developing interactive dashboards that demonstrate system performance and operational insights. In
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RF information and limited communication. A key contribution of the project is the design of frugal embedded control algorithms tailored to resource-constrained robotic platforms. These control