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should have a strong mathematical background, particularly in dynamical systems theory, and a keen interest in network science, and scientific computation. The student will gain invaluable experience
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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
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and deterministic AI outputs is critical. This requires robust design principles and architectural changes to reduce variability and integrate smoothly with industrial control systems. Enhancing
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Current modelling and simulations require either generic assumptions to be made for fluid dynamic based modelling leading to inaccuracies between modelled and experimental data or, intense
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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, curious individual to join an exciting PhD. This opportunity is generously funded by John Crane Ltd, a world-renowned engineering technology leader. Why This PhD? Impact Clean Energy's Future: Develop next
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load emulation, surface tribology and lubricants, contact mechanics or dynamical phenomena. This is an opportunity to work within a world-class multidisciplinary team within the Engineering Systems
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increasingly important. The aim of the project is to explore the collaborative dynamics of agents within eCPS, with a specific focus on aligning their behaviours towards achieving sustainability goals. Cranfield
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PhD Studentship available in the EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero (RAINZ) This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and