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Field
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-driven techniques, such as machine learning approaches, will enrich the scope of the project. Students engaged in this project will potentially have the chance to collaborate with industry partners in
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, including machine learning, to simulate CFRP's complicated response to various loading and environmental conditions. The development of the tools will require significant amount of computer programming using
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developments in artificial intelligence (AI), machine learning (ML) and microelectronics have enabled drones to work even more efficiently, enabling them to address emerging demands in several sectors including
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a strong background in Computer Science, Engineering, Maths or Physics, and preference would be given to those with a good understanding of computer vision and deep learning. It is essential to have a
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process efficiency improvements and enable prediction across scales for a given biological process. The PhD project will develop, construct and apply Machine Learning techniques to the process data
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of rotating machines for all industries. You would be working with the research centre in Manchester and the manufacturing site in Slough. Funding Notes The PhD is funded at the standard EPSRC rate (£19,237 in
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PhD Studentship: Centre for Doctoral Training in Composite Materials, Sustainability and Manufacture
predicting the effect that these defects have on structural performance. The occurrence of defects is a complex problem involving many factors. A combination of statistical tools and advanced machine learning
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(ideally in python or C/C++) Knowledge of neural networks and machine learning algorithms would be beneficial Good communication and writing skills Self-motivated to conduct research activities independently
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opportunity for students to dig into the realm of wind energy from a holistic perspective. The incorporation of data-driven techniques, such as machine learning approaches, will enrich the scope of the project
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This is a full-time, funded PhD opportunity, open to home and overseas students. Only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee