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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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Bhutan (covering 2013–present, at resolutions from 15-minute to daily). Objectives for this project are flexible, but include: Harmonising timestamps, units, and metadata across the seven national datasets
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process is poorly recorded and needs improvement. Aims and Objectives In collaboration with the Health Innovation Partnership, a modelling pipeline will be devised to cope with the challenges of data
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turbulence due to varying bathymetry, bed roughness, and due to boundary forcing due to free surface changes or fixed lateral channel boundary. The research objectives include designing an experimental
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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behaviour, improve body composition and modulate stress. However, people often find starting an exercise programme a daunting experience and there are many barriers that prevent women from getting lifestyle
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compliance and operational integrity. The application of AI in these areas enhances the ability to predict system behaviours, detect anomalies, and streamline certification workflows. AI-driven tools can
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of synthetic load profiles for domestic heat demand delivered via ASHPs and heat networks. Objectives: • To complete an evaluation of existing heat demand, based on available data sets; • To analyse
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coupled computational framework capable of predicting crack initiation, propagation, and component failure under realistic operating conditions. Key Objectives: - Develop a finite element-based chemo-thermo
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this by monitoring infectious disease trends and outbreaks using systems that collect information over time and allow unusual changes to be detected. While these normally count laboratory test results