Research Associate Multimodal AI for Translational Medicine

We are seeking a motivated and collaborative individual to join our team as a Research Associate Multimodal AI for Translational Medicine. This role offers an exciting opportunity to contribute to develop multimodal foundation models, generative artificial intelligence (AI) methods and synthetic health data applications for the Digital Infrastructure programme of the NIHR Manchester Biomedical Research Centre within a dynamic and inclusive environment.

Developing scalable, reproducible deep learning pipelines that process and integrate diverse healthcare data, including clinical variables, imaging, pathology and molecular data, to create multimodal foundation models across cardiovascular, cancer and respiratory research.

• Designing and building generative AI frameworks, using diffusion models, transformers and graph neural networks, to produce synthetic patient cohorts that preserve clinical usefulness while protecting patient privacy.
• Introducing new causal inference and longitudinal modelling approaches to advance patient trajectory prediction, treatment simulation and clinical decision support across multiple research themes.
• Working closely with clinical experts, partner NHS trusts, the Greater Manchester Care Record and the North West Secure Data Environment to ensure innovations become tools that support clinician scientists in real-world healthcare research. Publishing findings as first author in peer-reviewed journals and presenting at leading national and international conferences, with an emphasis on translational healthcare AI and computational medicine.

We welcome candidates who bring diverse perspectives, experiences, and approaches to their work.

We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate:

• A record of publications in high-impact AI, healthcare or computational medicine journals or conferences, demonstrating research excellence in translational AI applications.
• Proven ability to identify and solve complex healthcare AI challenges with innovative approaches, particularly in multimodal data fusion and clinical translation.
• Proficiency in modern foundation models, transformer architectures, diffusion models and multimodal learning for healthcare applications, with experience in longitudinal data analysis and causal inference.
• Expertise in Python and R for scientific computing, and in machine learning and deep learning frameworks such as PyTorch, TensorFlow and Hugging Face Transformers, and healthcare-specific libraries such as MONAI and TorchXRayVision.

We value transferable skills and real-world experience as much as formal qualifications.

For more information, please see University of Manchester Benefits. You can also find information on our Flexible and Hybrid working here.

We are an open place of enquiry and challenge. We embrace and celebrate difference, diversity and debate, and we pride ourselves on being a place of education, learning and community where we are able, within the law, to question and test received wisdom, express new ideas and explore controversial or unpopular topics and opinions. Find out more from our Freedom of Speech Policy.

Name: Professor Alex Frangi

Email Address: alejandro.frangi@manchester.ac.uk

recruitmentservices.people@manchester.ac.uk

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

This role is not eligible for Skilled Worker visa sponsorship. Applicants must demonstrate the right to work in the UK.

Applications close at midnight on the closing date.

Further particulars (with person specification) linked below.