36 assistant-professor-computer-"https:" "https:" "https:" "https:" "EURAXESS" PhD positions at Cranfield University
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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. Diversity and Inclusion at Cranfield We are committed to fostering equity, diversity, and inclusion in our CDT program, and warmly encourage applications from students of all backgrounds, including those from
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requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in
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to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development
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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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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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more research is required to establish the inter-dependencies between these properties with the help of high resolution images. In this research, different sets of composites material will be dynamically
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attracted intense interest from both academia and industry recently for its attempt to extract more useful information and knowledge from Big Data. Big Data analytics will help to develop more advance
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ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves