Research Associate in Multimodal Foundation Models and Generative AI for In-Silico Trials of Cardiovascular Device at The University of Manchester

We are seeking two ambitious and proactive Research Associates to be part of a multidisciplinary team, focusing on image-based multiphysics modelling of cardiovascular fluid dynamics and device-tissue interactions. The successful candidate will utilise clinical and experimental data to pioneer novel gen-erative AI and geometric deep learning approaches to create synthetic virtual patient cohorts from multimodal data. This role involves developing advanced algorithms and high-throughput workflows for crafting virtual populations and simulation-ready computational anatomy models, integrating tis-sue microstructure properties where relevant. The role requires applying innovative techniques to large, real-world multimodal datasets, including clinical trials and population imaging studies.

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.

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: Alex Frangi

Email: alejandro.frangi@mancheser.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.