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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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14 Nov 2025 Job Information Organisation/Company Cranfield University Department HR & Development Group Research Field Agricultural sciences Researcher Profile Recognised Researcher (R2) Positions
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14 Nov 2025 Job Information Organisation/Company Cranfield University Department HR & Development Group Research Field Environmental science Researcher Profile First Stage Researcher (R1) Recognised
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but will include visits and meetings within the Midlands region to gather base data, to conduct stakeholder engagement activities and key informant interviews. Partners and collaboration Water Resources
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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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subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal
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research community at WAMC, fostering collaboration and innovation. Additionally, there will be opportunities to work with WAMC’s industrial partners, such as WAAM3D (https://waam3d.com/ ) and members
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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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, forecasting and reporting to support the achievement of Facilities’ strategic objectives. You will work closely with Facilities budget holders to interpret financial information, explain variances, provide
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven