12757 - Research Fellow of Clinical AI and Health Equity
| Posting date: | 14 July 2025 |
|---|---|
| Salary: | £40,497 to £48,149 per year |
| Hours: | Full time |
| Closing date: | 04 August 2025 |
| Location: | Edinburgh, Scotland |
| Remote working: | Hybrid - work remotely up to 3 days per week |
| Company: | University of Edinburgh |
| Job type: | Temporary |
| Job reference: | 12757 |
Summary
CMVM / MGPHS / USHER Institute
UE07: £40,497 to £48,149
Full-time: 35 hours per week
Fixed Term available from 1st September 2025 until 31st January 2027
Location: Usher Institute, Edinburgh BioQuarter (EH16 4UX)
We will consider requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular (weekly) on-campus working. The Usher Institute expects a minimum of 40% on campus working.
The Centre for Medical Informatic at the Usher Institute within The University of Edinburgh is looking for a post-doctoral research fellow with expertise in machine learning techniques to solve real world problems and large-scale datasets, risk prediction models using large-scale real-world electronic health records and analysing and mining large-scale clinical data.
The Opportunity:
To join a team of experienced health data scientists, AI specialists, statisticians and clinical epidemiologists and contribute significantly to assess and mitigate data and AI induced bias from large scale national and local health record data resources. This role is part of the QMIA project https://gtr.ukri.org/projects?ref=MR%2FX030075%2F1 .
Informal enquiries may be directed to Honghan Wu, Professor of Health Informatics and AI, Honghan.wu@glasgow.ac.uk or Sarah Wild, Professor of Epidemiology, sarah.wild@ed.ac.uk.
Your skills and attributes for success:
• PhD or equivalent experience in computer science, informatics or a related discipline (e.g., artificial intelligence, machine learning)
• machine learning techniques on solving real world problems and large-scale datasets
• analysing and using large health datasets for risk prediction
• excellent oral and written communication skills, including peer-reviewed publications
• ability to work effectively and flexibly in a multi-disciplinary team
UE07: £40,497 to £48,149
Full-time: 35 hours per week
Fixed Term available from 1st September 2025 until 31st January 2027
Location: Usher Institute, Edinburgh BioQuarter (EH16 4UX)
We will consider requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular (weekly) on-campus working. The Usher Institute expects a minimum of 40% on campus working.
The Centre for Medical Informatic at the Usher Institute within The University of Edinburgh is looking for a post-doctoral research fellow with expertise in machine learning techniques to solve real world problems and large-scale datasets, risk prediction models using large-scale real-world electronic health records and analysing and mining large-scale clinical data.
The Opportunity:
To join a team of experienced health data scientists, AI specialists, statisticians and clinical epidemiologists and contribute significantly to assess and mitigate data and AI induced bias from large scale national and local health record data resources. This role is part of the QMIA project https://gtr.ukri.org/projects?ref=MR%2FX030075%2F1 .
Informal enquiries may be directed to Honghan Wu, Professor of Health Informatics and AI, Honghan.wu@glasgow.ac.uk or Sarah Wild, Professor of Epidemiology, sarah.wild@ed.ac.uk.
Your skills and attributes for success:
• PhD or equivalent experience in computer science, informatics or a related discipline (e.g., artificial intelligence, machine learning)
• machine learning techniques on solving real world problems and large-scale datasets
• analysing and using large health datasets for risk prediction
• excellent oral and written communication skills, including peer-reviewed publications
• ability to work effectively and flexibly in a multi-disciplinary team