194 structural-engineering "https:" "https:" "https:" "https:" "https:" "https:" "Multiple" "https:" positions at University of Manchester
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General enquiries: Email: recruitmentservices.people@manchester.ac.uk Technical support: https://jobseekersupport.jobtrain.co.uk/support/home Please quote reference BMH-030874 This vacancy will close
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@manchester.ac.uk General enquiries: Email: recruitmentservices.people@manchester.ac.uk Technical support: 0161 850 2004 https://jobseekersupport.jobtrain.co.uk/support/home This vacancy will close for applications
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part of a larger consortium of technology focussed scientists within the Centre working on developing novel toolkits and approaches for cutting edge extracellular matrix research. This is a full-time
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, shortlisting and interviews: Name: Nicholas Bell Email: Nicholas.bell@manchester.ac.uk General enquiries: Email: recruitmentservices.people@manchester.ac.uk . Technical support: https
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We are looking for an ambitious and talented postdoc to work on exciting synthetic genomics projects, such as the international Genome Project-Write (http://engineeringbiologycenter.org) and/or
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: Kellie.Gallagher@manchester.ac.uk General enquiries: Email: recruitmentservices.people@manchester.ac.uk Technical support: https://jobseekersupport.jobtrain.co.uk/support/home This vacancy will close
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enquiries: Email: recruitmentservices.people@manchester.ac.uk Technical support: https://jobseekersupport.jobtrain.co.uk/support/home Interviews scheduled to be held, in person, on 30 April 2026. This vacancy
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recruitment enquiries: recruitmentservices.people@manchester.ac.uk Technical support: https://jobseekersupport.jobtrain.co.uk/support/home Applications close at midnight at the end of the day on the closing
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reliability The Person The successful candidate should be able to demonstrate: Project Expertise: Expertise in managing complex technology or security projects from initial planning through to successful
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and materials engineering. They will do this by integrating modern data-centric approaches, such as physics-informed machine learning, structure-aware modelling, and digital-twin methodologies, with