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 university in Edinburgh is seeking a post-doctoral research fellow specialized in machine learning. The position involves analyzing large-scale datasets to develop risk prediction models and addressing AI-induced bias in health data. Applicants should hold a PhD and have experience in machine learning within a multi-disciplinary team environment. This full-time role offers competitive salary and benefits within a collaborative research setting.

Benefits

Competitive salary

Comprehensive Staff Benefits

Generous holiday entitlement

Qualifications

• Experience applying machine learning techniques to solve real-world problems.
• Experience analyzing and using large health datasets for risk prediction.
• Peer-reviewed publications.

Responsibilities

• Contribute to assessing and mitigating data and AI-induced bias.
• Join a team of experienced health data scientists.
• Analyze large-scale datasets for risk prediction models.

Skills

Machine learning techniques

Analyzing large health datasets

Excellent oral and written communication

Ability to work in a multi-disciplinary team

Education

PhD or equivalent experience in computer science, informatics, or a related discipline

Job description

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 large-scale real-world electronic health records and clinical data mining.

The Opportunity:

To join a team of experienced health data scientists, AI specialists, statisticians, and clinical epidemiologists and contribute 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 inquiries 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

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Please include your CV and a supporting statement detailing how you meet the knowledge, skills, and experience required for this post.

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As a valued member of our team you can expect:

• A competitive salary
• An exciting, positive, creative, challenging, and rewarding work environment
• A diverse and vibrant international community
• Comprehensive Staff Benefits, including generous holiday entitlement, pension schemes, staff discounts, and family-friendly initiatives. See our staff benefits page for details.

Championing equality, diversity, and inclusion

The University of Edinburgh holds a Silver Athena SWAN award and is a member of the Race Equality Charter and Stonewall Scotland Diversity Champions, actively promoting LGBT+ equality.

Prior to employment, you must evidence your right to work in the UK. International applicants requiring sponsorship must meet UK Home Office requirements and secure a Skilled Worker Visa.

Key dates:

The closing date for applications is 4 August 2025.

Application deadline is 11:59 pm GMT, adjusted for your time zone if applying from outside the UK.

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