50 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"SciLifeLab" positions at Cranfield University
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new role within our Student Systems Team, to provide technical development and analytic expertise across strategic development and core operational support of the student information management systems
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postharvest drying energy demand. Combining applied mycology, food safety modelling, precision agriculture and Net Zero energy systems, the research will deliver energy-efficient, data-driven grain storage
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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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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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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
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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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due to a lack of resource. With some water-hungry sectors (such as data centres and other high-tech industries) prioritised for significant growth in water stressed regions, these challenges are set to
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provide a bursary of £20,780 (tax free) plus fees* for three years. This opportunity is open to Home and Overseas fee status students. For further information: Name: Dr Abhijeet Ghadge Email
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significantly to the current body of knowledge. This experience will equip you with valuable research skills, including methodologies, data analysis, and critical thinking, highly sought after in both academic
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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