At a Glance
- Tasks: Join a global initiative to analyse data from wearable sensors for maternal and foetal health.
- Company: The University of Edinburgh, a world-class institution with a vibrant research culture.
- Benefits: Full-time role with opportunities for professional growth and collaboration.
- Other info: Collaborate with international partners in a dynamic research environment.
- Why this job: Make a real impact on maternal and neonatal health using cutting-edge technology.
- Qualifications: Degree in a scientific field and experience in data analysis and machine learning.
The predicted salary is between 35550 - 43450 £ per year.
School of Population Health Sciences / Usher Institute / Centre for Medical Informatics
Full-time: 35 hours per week
Fixed-term: available from July 2026 for 12 months
The Centre for Medical Informatics at the Usher Institute within The University of Edinburgh is looking for a Research Fellow (Data Analysis). This post offers the opportunity to work on an exciting new global collaborative initiative using wearable sensors and data driven research approaches to improve maternal and foetal outcomes during childbirth.
You will join a collaborative research project entitled, ‘Intelligent Pregnancy Products Platform; I3P’ to provide near-real time analysis of continuous physiological data and clinical event data in a large scale, international, cluster-randomised trial. You will work in a vibrant research environment collaborating with international stakeholders and industry partners to analyse the results of a trial of 13,400 women across 30 hospitals in 4 countries (India, Rwanda, Nigeria & Pakistan).
The IMPALA 2 trial will use novel techniques and wearable technologies to collect continuous vital signs from both the mother and baby. You will develop, evaluate, and deploy generative AI methods to predict the onset of maternal and neonatal complications. This body of work has the potential to reduce maternal and foetal complications on a global scale.
Your skills and attributes for success:
- A good degree and higher degree in a scientific or numerate subject, or able to demonstrate relevant and equivalent recent experience.
- Experience in using statistical programming for data analysis and machine learning/deep learning, using python or R.
- Experience in monitoring, analysing and dissemination of complex information.
- Experience developing, evaluating, or deploying generative AI methods, particularly in applied or clinical contexts.
- Ability to work well within a multi-disciplinary team, including international and industry partners.
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