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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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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data
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information about application please visit Applying for a research degree . Apply now
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with a wealth of social and networking opportunities. How to apply For further information please contact: Name: Prof. David MacManus Email: d.g.macmanus@cranfield.ac.uk Phone: +44 1234 754735 If you are
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with an appropriate person. For further information about application please visit Applying for a research degree . Apply now
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will have sound knowledge of data protection regulations and be experienced in co-ordinating events and activities. Experience of working within an Apprenticeship or Higher Education environment and
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approach (we’ll provide training for you), and designing the new apprenticeship. You will have knowledge of a subject matter related to AI, Machine Learning and/or Data Science, most likely in two or more of
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of CRM, intranet, research systems and student information systems (e.g. SITS) would be welcome. You will be tenacious, have excellent communication skills and demonstrate a flexible, enthusiastic and
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have some expertise in business analysis and techniques, such as ‘as is’ and ‘to be’, and swimlanes. Specific project experience of CRM, intranet, research systems and student information systems (e.g