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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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. As traditional jet engines approach the limits of their efficiency improvements, electric propulsion has gained significant attention as a transformative solution for reducing greenhouse gas emissions
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building typologies. This research aims to transform Pulse testing through AI integration—specifically leveraging descriptive, predictive, and generative modelling techniques—to enhance test accuracy
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Net Zero 2050 goals, electric motors must undergo a transformational leap—from today’s typical power densities of 2–5 kW/kg to a step-change 10–25 kW/kg by 2035. The highest power dense motors today
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The Faculty of Science AI Doctoral Training Centre (DTC) invites applications from Home students for fully-funded PhD studentships to carry out multidisciplinary research in the world-transforming
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the scalability and robustness of AI in complex environments which is a major step towards the digital transformation of the manufacturing industry. Motivation Automation is key to meeting the growing demand