36 parallel-computing-numerical-methods-"Simons-Foundation" PhD positions at Cranfield University
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott facility
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to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events alongside our Doctoral Researchers Core Development
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing and emerging applications, such as multi-domain autonomy and aerial mobility. With rising risks to...
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Resilience (WIRe) , a prestigious collaboration between Cranfield University, the University of Sheffield, and Newcastle University. The WIRe programme offers bespoke training that hones both technical and
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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are part of the programme. The research is funded by the Centre of Propulsion and Thermal Engineering at Cranfield University. The work will be conducted at the Cranfield icing wind tunnel (IWT) based
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benefit from an enhanced stipend of £25,726 per annum, undertake an international placement, and complete a bespoke training programme within a cohort of up to 15 students. Students will benefit from being
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big