70 high-performance-computing-postdoc positions at Cranfield University in United Kingdom
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nutrient removal with biodiversity benefits. Optimising these systems is critical to enhance their environmental performance, support regulatory compliance, and contribute to resilient, low-carbon water
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Cranfield University is seeking highly motivated and accomplished EngD students to conduct cutting-edge research in the degradation of materials at high temperature. We offer a fully funded program
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, operability and performance. This applies to podded as well as embedded propulsion systems. The standard approach to measure intake flow distortion is to use a relatively small number of total pressure probes
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temperatures in modern Gas Turbines. During the operation of gas turbines, such high temperatures are coupled with the impurities or ash compounds like Sulphur, halides, sodium and vanadium. In certain cases
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Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
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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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Energy storage and harvesting and Dr Lorenzo Conti , granular locomotion pioneer, will provide support across heat transfer modelling, computational simulation, microbial risk assessment and low-carbon
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customer journey and a consistent, high‑quality experience. This role is perfect for someone who thrives in a sales‑oriented environment where insight, initiative and relationship‑building make a measurable
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supply chain resilience. Following a PhD‑by‑papers route, the research aims to deliver three high‑quality scholarly outputs and develop insights to enhance supply chain management practise. This project
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chromatography-mass spectrometry (LC-MS) offer high sensitivity but are costly, time-intensive and unsuitable for rapid or field-based deployment. This project aims to address these limitations by developing a low