32 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"CEA-IBS" positions at Cranfield University
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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from passionate, skilled, committed, prospective experts in Data Science in Food Systems to join our team passionate in onion supply chain, to contribute in reducing losses due to fungal disease
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academic teams to align marketing efforts with client needs. You will combine strong analytical skills with creative flair, using data to optimise performance and help drive engagement and lead generation in
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for more information. •30 September 2024 •27 January 2025 •2 June 2025 •29 September 2025 We highly recommend you prepare the following information, as this will be requested at the application stage. Your
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, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems
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. You’ll be confident engaging with senior‑level stakeholders, translating product knowledge into compelling value propositions, and using data to inform decision‑making and identify opportunities. You will
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may be required to obtain UK Security Clearance. Funding This studentship is open to UK applicants only. How to apply For further information please contact: Name: Prof. David MacManus Email
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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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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