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Research Fellow in Mechanistic Models of Respiration for Patients in ICU at UCL

This appointment is part of the EPSRC funded CHIMERA hub, one of four Mathematical Sciences in Healthcare Hubs announced in March 2020. CHIMERA is a multidisciplinary Hub that focuses on understanding how to best support clinical teams in ICU in deciding how to support patients needing assisted ventilation (assisted breathing), typically using ventilators, and when to extubate.

CHIMERA brings together experts in mathematics, statistics, data science and machine learning, with unique, high volume and rich data sets from both adult and paediatric Intensive Care Units provided through embedded Project Partnerships with Great Ormond Street Hospital (GOSH) and University College London Hospital (UCLH). CHIMERA will deliver new mathematical frameworks combining artificial intelligence and mechanistic models (differential equations) to learn the biophysical relationships that govern the interdependencies -during normal and assisted respiration- between physiological variables, based on data sets for thousands of patients through these project partners.

The post is available for two years in the first instance.

Successful candidate will need to hold a PhD in Engineering, Mathematics or closely related discipline. Applicants should demonstrate a publication record appropriate to their level of experience.

The candidate should possess outstanding skills and experience in mathematical and/or computational modelling of physiological systems. They should also have knowledge of physiological modelling, ordinary and partial differential equations. They will have excellent skills in programming, using at least one of the following languages (R; Python, Julia, Matlab).

Applicants should apply online. To access further details about the position and how to apply please click on the ‘Apply’ button above.

If you have any queries regarding the application process, please contact Professor Vanessa Diaz (

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Our department is working towards an Athena SWAN award. We are committed to advancing gender equality within our department.

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