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Mamunur Rashid

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Fully Funded PhD in AI, Digital Health, and Bioinformatics for Sleep Signatures and Cardiometabolic Risk Prediction University of Birmingham in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD opportunity via the MIBTP program.

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Country

United Kingdom

University

University of Birmingham

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Keywords

Computer Science
Biomedical Engineering
Information Technology
Biology
Predictive Modeling
Artificial Intelligence
Health Disparities
Digital Health
Medical Science
Salud Pública
Type 2 Diabetes
Cardiometabolic Health
Statistics
Bioinformatic
Electronic Health Record

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About this position

A fully funded PhD opportunity is available in the Birmingham AI & Digital Health and Care Group at the University of Birmingham via the MIBTP program.

The project, SleeplessAI, sits at the intersection of artificial intelligence, digital health, bioinformatics, and electronic health records (EHRs). The research aims to discover latent sleep signatures and use them to improve cardiometabolic risk prediction, including conditions such as Type 2 Diabetes. A major focus is addressing health inequalities, especially within disproportionately impacted South Asian populations.

The successful applicant will work on predictive modeling, health equity, and community co-design to help translate fair and transparent AI into culturally sensitive, real-world clinical tools. This is a strong fit for candidates interested in responsible AI, healthcare analytics, and impactful translational research.

The post is explicitly described as fully funded. Interested applicants should apply through the MIBTP portal. For questions about the project, contact Mamunur Rashid at [email protected].

Funding details

Fully funded PhD opportunity via the MIBTP program.

What's required

A driven researcher is sought for a PhD in the Birmingham AI & Digital Health and Care Group via the MIBTP program. The post emphasizes interest in AI, electronic health records, predictive modeling, digital health equity, community co-design, and translating fair and transparent AI into real-world clinical tools. The applicant should be passionate about healthcare impact and working with culturally sensitive, health-inequality-focused research.

How to apply

Apply directly through the MIBTP portal using the provided link. For project-specific questions, email the supervisor at [email protected].

More information can be found here

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