63 data "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Cranfield University
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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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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are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here . The role of the Finance Professional Service Unit is to ensure that all financial data is
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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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. Attend food service briefings for awareness of allergens and any other relevant information related to daily menus. Follow all Food Handling and Hygiene guideline regulations at all times. Carry out any
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Information Work Location(s) Number of offers available1Company/InstituteCranfield UniversityCountryUnited KingdomGeofield Contact City Cranfield, Bedford Website http://www.cranfield.ac.uk/ Street HR and
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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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Apply online now at https://jobs.cranfield.ac.uk or contact us for further details on (E): peoplerecruitment@cranfield.ac.uk . Please quote reference number 5245. Closing date for receipt of applications
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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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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