478 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"CNRS-" PhD positions in United Kingdom
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experimental and computational, to understand and predict microtexture evolution during processing. The balance between experimental and computational research will depend on the profile of the successful
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with a background in chemical engineering, physics, soft matter science, computational chemistry, and/or related disciplines. Early applications are encouraged, as the position may be filled before
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emphasis on engineering realism. The ideal candidate will have: A strong background in aerodynamics, fluid mechanics, or turbomachinery Experience with RANS CFD (commercial or in-house solvers) Strong
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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radiative balance, but the physical mechanisms governing their formation particularly in the near-field region immediately downstream of aircraft engines remain poorly characterised. This fully funded PhD
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Doctoral Landscape Awards (IDLA) PhD position at the University of Nottingham addressing the specific engineering details of the wear of articulating splines for aeroengine applications. The successful
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Environment This studentship is based at the Centre for Vision, Speech and Signal Processing (CVSSP). CVSSP is: The largest UK research centre in its field Ranked 1st in the UK for Computer Vision research
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online via the above 'Apply' button. Under Campus, please select *Loughborough* and select Programme *Mechanical and Manufacturing Engineering*. Please quote the advertised reference number ‘SF-ST-2026’ in
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be made online . Under programme name, select * Mechanical and Manufacturing Engineering’* Please quote the advertised reference number: * FP-LJ-MTC/26* in your application. Applications must include a
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welcome motivated students with a strong background in engineering or science, such as physics, mathematics, computer science, or related disciplines. Skills in mathematics, computational modelling