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interference, while ensuring energy-efficient and scalable operation. This PhD project will focus on developing machine learning algorithms to enable robust channel estimation, intelligent user association
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for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and flight path analysis
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performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling
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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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, predictive maintenance algorithms, and digital twin technologies tailored specifically for healthcare, aviation, and sanitation industries. You will identify critical operational pain points within
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, optoelectronic, neuromorphic; Scale-up and Systems - Nanomanufacturing, cellular manufacturing, sensors /actuators, bioelectronics, IoT, theranostics; Frontiers in Nano-Metrology - in-situ nanometrology, electron
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capabilities needed for truly sustainable operations. Research Question: How can AI-enhanced digital twin technologies with advanced optimisation algorithms transform manufacturing processes to achieve
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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration with the Department
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advance the development of the Tool’s algorithms and functionality. As a key innovative component of D-Suite, this open-source tool will achieve wide industry visibility, and will be formally evaluated by