Research Associate in Multiphysics-Informed Machine learning for Cardiovascular Biomechanics

Job reference: SAE-028959
Salary: £37,694 - £46,049 per annum depending on experience
Faculty/Organisational Unit: Science and Engineering
Location: Oxford Road
Employment type: Fixed Term
Division/Team: Department of Computer Science
Hours Per Week: 35 hours per week (1 FTE)
Closing date (DD/MM/YYYY): 20/08/2025
Contract Duration: Fixed term for 36 months
School/Directorate: School of Engineering

Join our dynamic, multidisciplinary team as a Research Associate and make a transformative impact in scientific machine learning and digital twins for healthcare innovation! This role focuses on developing high-fidelity cardiovascular models, simulating fluid dynamics, and exploring device-flow and device-tissue interactions through cutting-edge multi-physics and physiological modelling.

You will leverage clinical and experimental datasets, apply physics-based simulations, and harness the power of scientific machine learning, including data assimilation and uncertainty quantification. Additionally, you'll work with large-scale multimodal datasets, clinical trials data, and population imaging studies to drive innovation in personalised medicine and in silico trials.

If you're passionate about pushing the boundaries of computational modelling and shaping the future of digital healthcare, this is the opportunity to bring your expertise to life!

What you’ll need

Applicants should have a PhD in computational cardiovascular mechanics and prosthetic valves or related fields and expertise in either computational solid mechanics to analyse soft-tissue deformations and device interactions or computational fluid mechanics to enable analysis of haemodynamics and thrombosis. One of the critical challenges we want to tackle is how to efficiently execute ensembles of virtual experiments entailing. Experience in working with multiphysics and multiscale models and in accelerated methods for solving partial differential equations and scientific machine learning (physics-informed machine learning) is essential. A developing publication profile will be advantageous.

As this role involves research at a postgraduate level, applicants who are not an EEA national or a national of an exempt country and who will require sponsorship under the Skilled Worker route of the UK Visas and Immigration’s (UKVI) Points Based System in order to take up the role, will be required to apply for an Academic Technology Approval Scheme (ATAS) Certificate and will need to obtain this prior to making any official visa application UKVI.

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

Our University is positive about flexible working – you can find out more here

Hybrid working arrangements may be considered.

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk.

Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Alejandro Frangi

Email: alejandro.frangi@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

Technical support:

https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

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