69 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions at Cranfield University in United Kingdom
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6 Jan 2026 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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or equivalent Additional Information Work Location(s) Number of offers available1Company/InstituteCranfield UniversityCountryUnited KingdomGeofield Contact City Cranfield, Bedford Website http
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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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the challenge of forever chemicals in drinking water. The aim of this research is to develop a smart data predictive model that will support utilities’ evidence-based decision-making to improve the resilience and
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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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experimental and operational data, evaluating machine performance, and preparing high-quality technical reports for industrial partners. The Research Fellow will also be expected to contribute to the wider
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information, as this will be requested at the application stage. Your proposal should demonstrate your interest and your potential to carry out independent research. Short two-page proposal that: Defines a
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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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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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, 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