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materials that combine the structural features and mobility in a single component. These are liquid crystal elastomers (LCEs), unique responsive materials that reversibly change shape, colour, adhesive
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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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Computational verification of high-speed multi-material flows, where physical experimentation is highly limited, is seen as critical by the Defence Sector (source: the UK Atomic Weapons
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the development of both, the quantum internet and distributed quantum computing. The objectives of this PhD thesis project are: (a) Demonstrate spin-photon entanglement with single colour centres in silicon carbide
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samples, by the development of advanced computational multiphysics models of stress corrosion cracking and coupling these with process-microstructure models (being developed within MicroAM project). Main
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level equivalent to a two-year master's degree. The ideal candidate will have a background in photonics and condensed-matter physics. You should have a passion for theoretical and computational physics, a
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NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science. The focus of this position is on
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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and electrochemical devices, by employing advanced experimental and theoretical methods in an interdisciplinary approach bridging synthetic chemistry, condensed-matter physics, and materials science
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devices, by employing advanced experimental and theoretical methods in an interdisciplinary approach bridging synthetic chemistry, condensed-matter physics, and materials science. Our research encompasses