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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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This exciting opportunity is based within the Mechanical and Aerospace Systems (MAS) Research Group at Faculty of Engineering which conducts cutting-edge research into robotics, control, and
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second class UK honours degree or equivalent in a related discipline. This project would suit students with an aerospace or mechanical engineering background. Experience of computational fluid dynamics
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we are looking for The candidate should have a 1st or high 2:1 degree in mechanical/aerospace/manufacturing engineering, computer science, physics, mathematics, or related scientific disciplines
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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degree in Aerospace Engineering, Mechanical and/or Structural Engineering, or a closely related field. Solid knowledge of solid mechanics, computational mechanics, or structural analysis. Demonstrated
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researcher you will be involved in challenging research projects. Qualifications You must have a master’s degree or equivalent in in aerospace engineering or a related subject and previous experience in
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a role of critical national importance: helping deliver the UK’s nuclear deterrent. We are at the forefront of science, technology and innovation, protecting the UK and NATO allies from the most
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status rate at the School of Mechanical, Aerospace and Civil Engineering at the University of Sheffield under the supervision of Professor Pierre Ricco (www.pierre-ricco.co.uk ). Additionally, the student
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion