39 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Cranfield University
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with intelligent technologies. These agents will enable the creation of dynamic, evolving services across various sectors, including healthcare, urban intelligence, and education, fostering continuous
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of novel AM materials on corrosion response of key component and develop a model to predict their behaviour. To address the goals set for tackling international climate change, the power sector needs
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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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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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. The project offers a rare mix of theoretical development, simulation-based research, and experimental validation, supported by advanced testbeds and datasets. Students will also be encouraged to present results
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dynamically combine trusted and opportunistic signals. This project aims to develop versatile benchmarks for assured multi-domain PNT systems with advanced integrity frameworks, enabling rigorous evaluation and
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience