Research Associate in Multimodal Generative AI for Virtual Patient Populations in In-Silico Trials

Job reference: SAE-028957
Salary: £37,694 to £46,049 per annum, dependent on relevant experience
Faculty/Organisational Unit: Science and Engineering
Location: Oxford Road
Employment type: Fixed Term
Division/Team: Department of Computer Science
Hours Per Week: Full Time (1 FTE)
Closing date (DD/MM/YYYY): 20/08/2025
Contract Duration: 36 months
School/Directorate: School of Engineering

We are looking for an ambitious Research Associate who is passionate about generative AI, synthetic data, and in-silico trials to join our multidisciplinary team. You will work with colleagues who are experts in cutting-edge image-based multiphysics modelling of cardiovascular fluid dynamics and device-tissue interactions.

You will work with clinical and experimental data to develop innovative geometric deep learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows for crafting simulation-ready computational anatomy models, incorporating tissue microstructure properties.

This is an exciting opportunity to apply your expertise to large-scale real-world datasets, including clinical trials and population imaging studies, and make a transformative impact in computational modelling and healthcare innovation.

Keywords: Generative AI, synthetic data, in-silico trials, virtual patient cohorts, geometric deep learning, computational anatomy, cardiovascular modelling, multimodal datasets.

What you’ll need

Applicants should have a PhD (or nearing completion) in computational imaging and deep learning, and an understanding of applied mathematics, focusing on algorithm design and analysis. Proficiency in modern ML techniques, including geometric deep learning, diffusion models, and neural networks for multimodal image analysis will be essential, as well as expertise in Python and C/C++ for scientific computing, and in ML/DL frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn. 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 support an inclusive working environment and welcome applicants from all sections of the community regardless of age, disability, ethnicity, gender, gender expression, religion or belief, sex, 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 CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Prof 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.

Please be aware that due to the number of applications we are unfortunately not able to provide individual feedback on your application.

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