Job Description

Job number

MED05514

Faculties

Faculty of Medicine

Departments

National Heart & Lung Institute

Salary or Salary range

49,017 - 57,472 per annum

Location/campus

White City Campus - Hybrid

Contract type work pattern

Full time - Fixed term

Posting End Date

16 Dec 2025

About the role

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Join an interdisciplinary team of researchers from Imperial College London, A*STAR Singapore and the University of Manchester in applying advanced machine learning to transform how pulmonary hypertension is diagnosed and treated. The Research Associate will develop and apply probabilistic models to predict molecular and clinical trajectories from longitudinal patient data, working closely with clinicians, biologists, and data scientists across the UK and Singapore. This is an exciting opportunity to advance precision medicine through cutting-edge AI and translational research.

What you would be doing

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As a Research Associate in Machine Learning, you will play a key role in developing innovative AI models that uncover how molecular and clinical features of disease evolve over time. You will design and implement probabilistic frameworks--such as Gaussian Process models--to analyse complex, high-dimensional data from patient cohorts. Working closely with clinicians, biologists, and computational scientists across Imperial, Manchester, and A*STAR Singapore, your work will help translate molecular insights into predictive tools for precision health. You will also have the opportunity to publish in leading journals, present at international conferences, and shape the next generation of data-driven discovery in cardiopulmonary research.

What we are looking for

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A PhD (or near completion) in computer science, statistics, biomedical engineering, or a related discipline.

Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range 43,863 - 47,223 per annum

Strong experience in statistical modelling, probabilistic machine learning, or time-series analysis (e.g. Gaussian Processes, deep learning). Proficiency in Python or R for data analysis and model development. An understanding of molecular or clinical health data and enthusiasm for interdisciplinary research. Excellent problem-solving, communication, and teamwork skills, with the ability to work effectively across computational and biological domains. A proactive approach to research, with curiosity, creativity, and a commitment to producing high-quality, impactful science.

What we can offer you

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This role offers an excellent opportunity to contribute to a high-profile, interdisciplinary research programme at the interface of AI, medicine, and molecular biology. As part of this position, you will benefit from:


Working within Imperial's National Heart and Lung Institute at the White City Campus, a leading cardiovascular centre for data-driven research. Collaborating with world-class researchers across Imperial College London, the University of Manchester, and ASTAR Singapore.
Playing a central role in a UK Research and Innovation project pioneering machine learning models for longitudinal multi-omics and clinical data. Gaining experience in translational research that bridges computational discovery and clinical application. Opportunities to publish in high-impact journals, present at international conferences, and build your research profile. Access to state-of-the-art computing facilities and a supportive, collaborative research environment that values professional growth and interdisciplinary training. The opportunity to continue your career at a world-leading institution Sector-leading salary and remuneration package (including 39 days off a year)

Further information

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This is a full time, fixed-term role (12 months) with the possibility of extension based on project progress and funding availability. This role is based at our White City Campus.


If you require any further details on the role please contact: Dennis Wang [dennis.wang@imperial.ac.uk]

Available documents

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Attached documents are available under links. Clicking a document link will initialize 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.



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