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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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: At least an upper second-class degree (preferably MSc) in a Science or Technology discipline. Good working knowledge of machine learning and deep learning. Hands-on knowledge of Python or PyTorch
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and programmable biomaterial synthesis. The ability to program the behaviour of biomolecular chemistry is foundational for developing new biotechnology applications. Redox-sensitive molecules are a
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farms at relatively close proximity can be relevant when considering their annual energy production. This project will examine the uncertainty of various types of numerical models, from fast-computing
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that benefit fitness in the opposite sex. You will receive outstanding interdisciplinary training in computational biology, experimental genetics, and molecular biology. The supervisory team offers expertise
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degree or international equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently
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the above ‘Apply’ button. Under Campus, please select ‘Loughborough’ and select the Programme “Mechanical and Manufacturing Engineering”. Applications must include a CENTA studentship allocation form , a
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21 Oct 2025 Job Information Organisation/Company The Institute of Cancer Research Research Field Biological sciences » Biology Medical sciences Mathematics » Computational mathematics Researcher
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Academy’s Training Programme, with access to bespoke courses developed for Engineering PGRs, including sessions on career development after the PhD. The faculty has outstanding facilities and works in
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Engineering, Faculty of Engineering and Applied Sciences, Cranfield University, in the area of performance simulation, analysis, and optimization of supercritical CO2 power generation systems. Cranfield has