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in materials research is important, experience in nanofabrication, micromechanical testing, and numerical simulations is desirable. Our offer You will be enrolled in the doctoral program in Mechanical
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materials. The computer modelling of LSP remains challenging due to its multi-physics and multi-scale nature. The dependency of the process on the shape of the laser pulse, its energy, ablation layers etc. is
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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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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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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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experts can reduce the environmental impact of the chemical and agricultural industries, offer eco-friendly analytical techniques, and assess the safety of new materials. The application process in
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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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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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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