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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
We are pleased to announce a self-funded PhD opportunity for Quantitative assessment of damage in composite materials due to high velocity impacts using AI techniques. Composite materials, such as
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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sponsors to deliver the outputs and will have access to a bespoke training programme. Per- and polyfluoroalkyl substances (PFAS), also known as “Forever Chemicals”, are micropollutants of increasing concern
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in a relevant disciplines in Engineering (for example, Chemical, Material or Process Engineering), Physical Sciences, Economics and Business, or Environmental Science and Sustainability. Prior
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of first or second class UK honours degree or equivalent in a related discipline, science (chemistry/physics) or engineering. The ideal candidate should have some understanding in the area of materials
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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mechanical, control or aerospace engineering, physics, mathematics, or other relevant engineering/science degree. The ideal candidate would have experience with computational modelling and control of dynamical