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We are seeking a Research Fellow to develop AI-enabled cloud analytics and BIM-based Digital Twin systems for real-time geometry monitoring and control in bridge and viaduct construction. Key
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078v2) Start: Between 1 October 2026 and 1 July 2027
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to improve cloud scheme that contributes one of the most important uncertainties in climate projections (Wang et al 2026, Nature Communications). Wang et al (2026) found that climate models largely
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cloud and HPC platforms to provide powerful compute capabilities to our users. In this role, your contribution will help to deliver the Franklin’s mission to drive forward technology development in
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Start Date: October 2026 Application deadline: 30th April 2026 Introduction: Warm cumulus clouds have been observed to yield precipitation as soon as 30 minutes after their formation. Formation
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performance storage for I/O intensive compute tasks Store research data safety and securely, directly accessible to HPC and research platforms and easily accessible to researchers Development of cloud services
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will have a PhD (or close to completion) in bioinformatics, health data science/computer science, or a closely related discipline, in the relevant subject area for the research programme, with
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increasingly important for autonomy, navigation, inspection, and situational awareness across defence and other safety-critical applications. Yet many real-world deployments cannot depend on cloud computing
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will have A PhD (or equivalent) in an area pertinent to the subject area, i.e. in Computer Science, Electrical Engineering and Electronics or Computer Science with relative experience in firstly AI and
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battery data. Develop scalable, secure, and production-grade software systems supporting industrial deployments. Integrate embedded BMS firmware, edge computing devices, and cloud analytics pipelines