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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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, balancing efficiency and sustainability in AI deployment poses a significant challenge, calling for advances in model design and training to reduce environmental impact while maintaining high performance
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations
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prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection, diagnostics, and prognostics in power
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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manufacturing methods and environmental storage conditions on the material properties and performance over time. The aim of this project is to understand how the properties of polyolefins (such as polyethylene
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on modelling and testing of new reactors with a view to optimising the best systems for mixing supercritical water (>378o C and 221 bar) with wastewater feed streams. This needs to generate residence times
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rely on unsustainable materials and on carbon-intensive manufacturing processes. This is posing major environmental and ethical challenges. The project will motivate the PhD student to develop next
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This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach
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experimental methods to improve them, alongside developing new approaches to enhance these properties. External environmental factors, such as humidity, pressure, and temperature, will also be key variables