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sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits
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faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies
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’–mechanical networks built from many sensors and actuators that locally communicate with one another to achieve collective functionality. These active networks could enable next-generation bioinspired robots
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our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors
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dynamics, control algorithms and sensor integration can be designed as a coherent system to improve speed, range, maneuverability, response time and operational robustness while also having underwater
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healthcare, communication and sensing, and have the potential to be disruptive to the whole society. A diversity of PIC-based sensors have been proposed, such as environmental sensors (e.g. gas sensing
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors
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for lifetime performance modelling. The key challenges are the robust integration of a sensor system in the structure and the analysis of measured signals, which are typically strongly affected by environmental
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per year, subject to annual increases. About the Project (Background & Methodology) Autonomous systems such as drone fleets, mobile robots, and sensor networks increasingly use federated learning (FL
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, including physics-informed neural networks, neural operators, hybrid physics-ML approaches, and emerging foundation-model paradigms for scientific data. Scientific machine learning is increasingly important