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subsystems • High performance tunable and reconfigurable oscillators and frequency synthesisers • Application of AI / Machine Learning to physical layer circuitry, signals and waveforms Researchers can expect
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various applications. Potential applications include environmental monitoring, process manufacturing, machining, scientific characterisation, and renewable energy systems. The outcomes of this research will
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and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment. Learn More: The Dynamics Research Group
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-based models (based on machine learning, agent-based, etc) primarily to: i. predict energy demand in multi-energy systems (airflow, electricity, heat) ii. dynamically manage building systems to maintain
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themes of “digital research”. The research elements of the post will include the application of big data approaches, artificial intelligence and machine learning to neurodegenerative disease drug design or
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Early-stage failure prediction in fusion materials using machine learning
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of wear and fatigue failures – design life is 20 years but bearings rarely last that long (3). As machines have got larger, this state has worsened. Manufacturing very large bearings (up to 5m diameter) is
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application in a working environment in some of the following: Insect neuroscience/behaviour; Electrophysiology; Data/image/video analysis; Drosophila genetics; Electron microscopy; Computer programming
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and monitoring data can be used to develop machine agnostic process control methodologies. Perform metallographic and non-destructive assessment of LPBF builds to assess and classify build quality
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granulation process. The aim of this project is to use Industry 4.0 technologies including machine learning and artificial intelligence (AI) to develop digital and soft sensors to predict product properties and