12757 - Research Fellow of Clinical AI and Health Equity

University of Edinburgh; City of Edinburgh; Hybrid; GBP 40,000 - 49,000; Full time

Job summary

A leading research institution is seeking a post-doctoral research fellow to apply machine learning techniques to real-world health problems at their Usher Institute in Edinburgh. Candidates should hold a PhD in computer science or related fields and show experience in analyzing large-scale health datasets. This role promotes collaboration among data scientists and provides opportunities to impact healthcare outcomes significantly.

Qualifications

• PhD or equivalent experience in computer science or related discipline.
• Experience in applying machine learning to real-world problems.
• Experience analyzing large health datasets for risk prediction.

Responsibilities

• Contribute to mitigating data and AI-induced bias in health record data.
• Work within a team of health data scientists and AI specialists.

Skills

Machine learning techniques

Data analysis

Communication skills

Team collaboration

Education

PhD or equivalent experience

Job description

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 Informatics 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 analyze large-scale datasets, including risk prediction models using real-world electronic health records and mining large-scale clinical data.

The Opportunity:

Join a team of experienced health data scientists, AI specialists, statisticians, and clinical epidemiologists to contribute significantly to assessing and mitigating 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, at [e-mail] or Sarah Wild, Professor of Epidemiology, at [e-mail].

Your skills and attributes for success:

• PhD or equivalent experience in computer science, informatics, or a related discipline (e.g., artificial intelligence, machine learning)
• Experience applying machine learning techniques to solve real-world problems and analyze large-scale datasets
• Experience analyzing 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