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Postdoctoral Research Associate in Multimodal Foundation Models for Healthcare (Two Posts)

Imperial College London - Department of Bioengineering

Location: London
Salary: £49,017 to £57,472 per annum
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 19th May 2026
Closes: 16th June 2026
Job Ref: ENG03884
 

Location: South Kensington Campus, London

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 • Biosignal or time-series modelling • 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.
  • Access to uniquely rich, nation-scale healthcare and biomedical data resources, and millions of GPU hours on national AI compute infrastructure for ambitious large-scale experiments. https://www.imperial.ac.uk/early-career-researcher-institute
  • Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further Information

*Please 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 – a.faisal@imperial.ac.uk

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