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Field
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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Interested in working on a the future of optical inertial sensing for mechatronic vibration control? Join our team! Job description The CHiPS (Compact High-Precision Sensors) project aims
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seeking a highly motivated PhD student to join our team to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the candidate will be
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the Internet of Things (IoT), where networked sensors and actuators enable real-time adaptation to environmental changes. Consider a self-adaptive IoT network such as a smart home that autonomously manages
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neuromorphic ultra-low-power active sensor readout and processing at the edge. The chip design will enable online learning capabilities, aiming at modulating the spatio-temporal filtering properties with
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of Higher Education and Research (MESR). PINNACLE: Physics-Informed Neural Networks for Accelerated Cloud Light-Scattering Emulation Artificial intelligence is profoundly transforming atmospheric
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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heterogeneous and opportunistic sensor networks. Therefore, such an approach may significantly improve rainfall and runoff predictions. Research goals: Our primary goal is to improve the accuracy and prediction
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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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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design