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on the electronic properties of the investigated heterojunctions. While we will mainly use density functional theory (DFT) to achieve these goals, we will also exploit machine-learning techniques to train more
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industry's standard titanium alloy due to its strong balance of properties and low density. However, future aerospace designs demand materials with enhanced performance. Recent research suggests that reducing
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greater stress. The understanding of the relation between the material microstructure – grain structure, grain orientations, defects – and the in-service performance of the wheel is limited at present
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in the setting of one-dimensional holomorphic dynamics (the iteration of functions of one complex variable). For polynomials and rational functions, hyperbolic functions are well-understood: Such a
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PhD Studentship: Artificial Intelligence for Building Performance – Optimising Low-Pressure Airtightness Testing Supervisors: Dr Christopher Wood (Faculty of Engineering) and Dr Grazziela Figueredo
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Build Test Solutions Ltd (BTS). This is a fantastic opportunity to work towards a PhD whilst working with both academia and industry. We are looking for a self-motivated student, with an inquiring mind
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understand and characterise the real-world variability of conditions for chain drives in high performance sport. Improving the emulation of race conditions improves the optimisation of components for riders
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that align with Net Zero targets without compromising torque-speed performance. Aim This PhD project aims to develop a new generation of electric machines optimised for sustainability across the full lifecycle
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balance between weight and structural integrity is essential so that safety and performance are not compromised whilst maintaining material efficiency. Consequently, novel approaches to product design, such
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that these Pu rich islands and the degree of neutron poison homogeneity will affect the performance under disposal conditions, where issues such as aqueous durability are a key concern due to groundwater