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unique opportunity to gain expertise that is highly sought after across pharmaceutical and advanced manufacturing industries. This project is part of the EPSRC CDT in Process Industries: Net Zero . The
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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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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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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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be an integral member of the wearables group based at Oxford, led by Aiden Doherty. Our research team has access to world-leading population health data sets with objective wearable sensor measurements
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Entanglement distribution is a foundational capability for quantum networks, enabling diverse quantum technologies including—but not limited to—quantum key distribution (QKD). QKD provides
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shaping the epigenetic landscape on chromatin. Mutations and dysregulation in transcriptional regulators are a hallmark of cancer, where rewiring of enhancer networks impacts cancer proliferation
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global data such as commodity prices and weather predictions. Technological progress has made it possible to automatically collect a variety of sensor data and self-reported practice. The challenge is to
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nuclear facilities make wireless communications difficult. Expected Outcomes The PhD aims to deliver: Novel technologies enabling safe human-robot collaboration New safety and assurance methodologies
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-frequency eddy current sensor validated against real ROT measurements; (3) a real-time physics-constrained state estimator integrating JMAK transformation kinetics with the EM forward model; and (4) a model