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student will develop a strong combination of technical and transferable skills, including experimental design, data analysis, and process evaluation. In addition, they will gain experience in problem
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optimization of plant-mycorrhizal symbioses for more efficient P use in agriculture. The modelling component will involve combining existing models of root system growth, rhizosphere processes and P uptake
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temperature field, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across
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insights for diagnosing and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that
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in-process geometric monitoring. This is ideal for candidates interested in digital manufacturing, computer vision, and solving complex, real-world engineering challenges. Wire-arc Directed Energy
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circular resource recovery. Negative emissions technologies. Techno-economic, life cycle, and process modelling for decision support. CFD including of complex multiphase flow. Simulation of wind
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benefit from personal academic development throughout the process with support from supervisors wider research activities. There are no examinations on this programme, the final outcome will be a research
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. A typical caveat of data-driven modelling using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate
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-based additive manufacturing, arc and laser directed energy deposition, thermal–mechanical modelling, and intelligent process monitoring and control for fusion energy applications. We welcome
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materials science, coating deposition process technology, and device engineering for non-flat optics. The main objectives and tasks are as follows: Materials Engineering: Critically review literature