Research Associate in Multimodal Foundation Models for Healthcare

Job number ENG03884 Faculties Faculty of Engineering Departments Department of Bioengineering Salary or Salary range £49,017 - £57,472 per annum Location/campus South Kensington and White City Campus - On site only Contract type work pattern Full time - Fixed term Posting End Date 28 Jun 2026

About the role

Are you an ambitious researcher ready to shape the future of AI for healthcare? Nightingale AI at Imperial College London is seeking two outstanding Postdoctoral Research Associates to help build next-generation multimodal foundation models that can learn from the full richness of health data - from biosignals and electronic health records to imaging, wearables and biomedical knowledge. These posts offer a rare opportunity to work on frontier AI with real clinical and biomedical consequence.

What you would be doing

You will join Nightingale AI, an interdisciplinary programme spanning machine learning, medicine, neuroscience, engineering and translational healthcare. Depending on your expertise and interests, your research may focus on one or more of the following areas:

• Multimodal foundation models for biosignals and population-scale health data, including self-supervised learning, time-series modelling and cross-modal representation learning.
• Scalable generative health AI, knowledge-graph enhanced modelling, retrieval-augmented generation and architectures that improve faithfulness and scientific coherence.
• Theory of unified multimodal foundation models, including representation structure, scaling behaviour, modality alignment and mathematically principled approaches to heterogeneous data integration.

We are particularly interested in applicants who can help define the scientific direction of the programme, rather than simply execute a pre-specified agenda.

What we are looking for

You should hold or be close to completing a PhD in machine learning, artificial intelligence, computer science, statistics, mathematics, computational biology, biomedical engineering or a closely related quantitative discipline. We are looking for candidates with a strong track record and expertise in several of the following:

• Multimodal learning, self-supervised or representation learning.
• Large-scale or generative foundation models.
• Knowledge graphs, graph learning or retrieval-augmented methods.
• Machine learning theory, scaling laws or generalisation in deep learning.
• Distributed training and large-scale experimental ML.
• Experience with healthcare or biomedical data is highly desirable, but exceptional candidates from adjacent AI fields with a strong motivation to work in health are also encouraged to apply.

What we can offer you

• The opportunity to work on AI intended to matter in practice, not only on benchmark problems, as part of an ambitious programme building a new class of unified health AI systems.
• The chance to collaborate with researchers across machine learning, healthcare, neuroscience, engineering and translational medicine, and to publish top-tier research with a genuine path toward real-world impact.
• Sector-leading salary and remuneration package (including 41 days off a year and generous pension schemes).

Further information

Note that this is a PhD level role but candidates who have not yet been officially awarded will be appointed as a Research Assistant (£43,863 - £47,223).

Nightingale AI sits within the Departments of Computing and Bioengineering at Imperial College London, spanning a wider ecosystem of partnerships in healthcare, biomedical research and real-world deployment. For the right candidates, these roles offer the chance to help build not just models, but an entire new scientific and technological capability for healthcare AI.

This is a full-time, fixed post for 24 months (35 hours per week).

If you require any further details about the role, please contact Professor Aldo Faisal - .

Attached documents are available under links. Clicking a document link will initialise its download.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert prior to the closing date stated, should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.

If you encounter any technical issues while applying online, please don't hesitate to email us at . We're here to help.

EEO Statement

We work towards equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. We welcome applications from the Armed Forces community.