Research Associate or Senior Research Associate in Pulmonary Arterial Hypertension (PAH)

Imperial College London - National Heart & Lung Institute

About the role:

This is an exciting opportunity to join the GSK–Oxford–Imperial Modelling-Informed Medicine Centre (MiMeC) and develop multiscale mechanistic models of pulmonary arterial hypertension (PAH). This is a unique opportunity for an ambitious biomedical engineer wishing to develop their research with a multidisciplinary focus, working at the intersection of Artificial Intelligence, data science and biomedical modelling.

This post is funded by Glaxo Smith Kline Research and Development Ltd (GSK) and forms part of the MiMeC Pulmonary Vascular Theme and will focus on mechanistically modelling the imbalance between the TGF-β–activin–nodal and BMP–GDF branches of the TGF-β superfamily, which is now recognised as central to PAH pathogenesis.

You will work within Professor Steven Niederer’s computational physiology group at Imperial College London’s National Heart and Lung Institute, collaborating closely with scientists and clinicians from Imperial, Oxford, and GSK. The position offers the opportunity to combine mechanistic modelling, AI-assisted data integration, and simulation to build model-informed efficacy and safety tools for use in preclinical and clinical applications.

Please indicate in your supporting statement which job description best aligns with your background and expertise. Candidates will be considered for the appropriate level of seniority based on their experience and qualifications.

What we are looking for:

• A PhD and equivalent experience in Biomedical Engineering, Computational Physiology, Systems Biology, or a related quantitative 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.
• Experience in computational physiology, mechanistic modelling, or systems biology of organ function or chronic disease.
• Understanding of pulmonary vascular biology and PAH pathogenesis.
• Strong computational and analytical skills, with proficiency in Python or Julia.
• Experience in statistical modelling, parameter estimation, or uncertainty quantification (e.g. Bayesian inference or global sensitivity analysis).
• Interest in translational pharmacology, computational modelling of PAH disease progression and treatment, and a willingness to engage with industrial and academic collaborators.
• Excellent communication skills and a strong publication record.

What you would be doing

• Undertaking high-quality research in computational modelling of PAH disease progression and treatment.
• Developing and applying multiscale mathematical models linking molecular signalling, cell–matrix interactions, and vascular function.
• Integrating in vitro, in vivo, and clinical data to connect molecular and cellular pathways to organ-level functional decline.
• Contributing to the translation of mechanistic models for efficacy prediction, patient stratification, and therapeutic optimisation.
• Applying statistical, AI, and machine-learning methods for model calibration, validation, and uncertainty quantification.
• Working closely with collaborators at GSK, Oxford, and Imperial to align model outputs with preclinical and clinical efficacy studies.
• Delivering reproducible, open-source modelling tools and publishing results in high-impact scientific journals.

What we can offer you:

• Supporting you in developing your career into an independent researcher at a world-leading institution
• Working with the Cardiac Electro-Mechanics Research Group led by Prof. Steven Niederer
• Sector-leading salary and remuneration package (including 39 days off a year)

Further Information

This is a full time, fixed term role (3 years) with the possibility of an extension.

Please indicate in your supporting statement which job description best aligns with your background and expertise. Candidates will be considered for the appropriate level of seniority based on their experience and qualifications.