658 engineering-computation "https:" "https:" "https:" "Univ" PhD positions in United Kingdom
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Supervisors: Dr Yaoyao Zheng , Prof. Hao Liu , Dr Omid Saghafifar (Remedium ) Programme Length: Four years Prospective Start Date: October 2026 Net2 Zero Centre for Doctoral Training The EPSRC and
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j.m.warnett@warwick.ac.uk Essential and Desirable Criteria Essential : Applicants should hold or expect to gain a minimum of a 2:1 bachelor’s degree or equivalent in Computer Science, Engineering, Physics
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the wind/renewable energy training and research elements of the CDT programme. Funded by ESB and EPSRC, this 4 year this PhD studentship, at the University of Strathclyde is in the area of engineering and
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or mechanical engineering, specifically on end of life and life extension analysis for wind farms. The role is for the third intake of the EnerHy programme which commences in October 2026. Research project
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manufacturer and technology company. Applications are invited for a 3.5-year EPSRC funded UDLA PhD studentship. The studentship will start on 1st October 2026. Project Description As Deep Learning and
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the Computational Science and Engineering Group’s (CSEG) goals and will proceed as a key foundational project for the new BASE Laboratory. You will be fully embedded within the M3 4Impact doctoral
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to funding stipulations set by UKRI, we are able to recruit up to 30% of international applicants to the cohort each year. You can find further details at https://www.centre-ub.org/studentships/call-for
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to funding stipulations set by UKRI, we are able to recruit up to 30% of international applicants to the cohort each year. You can find further details at https://www.centre-ub.org/studentships/call-for
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that you apply early as the advert may be removed before the deadline. This PhD project aims to develop a virtual tabletting laboratory by creating computational models that capture the multiscale mechanics
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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty