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area of Mechanics and Materials is interpreted widely, encompassing experimental, theoretical and computational methods. However, it is expected that the PhD will be supervised by a member of Academic
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Richard Feynman famously quoted ‘turbulence is the most important unsolved problem of classical physics’. Understanding and modelling turbulent flow physics will unlock a vast potential for optimising many applications of the energy...
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PhD Studentship in Aeronautics: Smart Adaptive Materials for Soft Robotics Skins and Aero-robotic Applications (AE0076) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Soft robotics is a rapidly advancing field with applications ranging from advanced manufacturing to space...
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combination of experimental work, rig development, and computational modelling, and will equip the student with a unique skill set highly relevant to both academic research and industrial careers such as
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candidates with a background in meteorology, climatology, physics, and any related discipline, and a strong interest in applying advanced physical and computational methods to real-world challenges in the area
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. This research is ideally suited to candidates with interests in photonics, metamaterials, ultrafast optics, nanofabrication, and computational electromagnetism. Strong coding (Python /MATLAB) and experimental
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‘Campus’, please select ‘Loughborough’ and select the ‘Programme’ as ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number ‘FP-HZ-2025’ in your application under
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to completion of the PhD programme. The DTP will reserve up to 3 ring-fenced scholarships for home students from certain Black, Asian & Minority Ethnic groups. The eligibility criteria for these ring-fenced
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(PhD)” on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form. Candidate requirements: A first or high 2:1 honours
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ODEs with convergence guarantee and uncertainty quantification,” Mathematics of Computation, Jun. 2025, doi: 10.1090/mcom/4120, https://arxiv.org/abs/2404.19626 C. Offen, S. Ober-Blöbaum, “Learning