Uazman Alam
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Fully Funded PhD in Diabetes Cardiovascular Health, Wearable PPG and AI-Enabled Digital Health University of Liverpool in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
The PhD opportunity is described as fully funded under the MRC NW-DMS DLA programme. No stipend amount or specific tuition-fee details are provided.
Country
United Kingdom
University
University of Liverpool
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About this position
This fully funded PhD project will investigate innovative approaches to improve the detection of cardiovascular autonomic neuropathy in people with diabetes. The research will focus on advanced analysis of wearable photoplethysmography (PPG) data, combining digital health, wearable technologies, biomedical signal processing and clinically relevant cardiovascular research.
The student will work at the intersection of academia, industry and the NHS on a translational project with potential to improve patient care. The research environment will support the development of expertise in time- and frequency-domain analysis of physiological time series, machine learning applied to sensor data, data science and AI-enabled health technologies.
The project is hosted at the University of Liverpool and is associated with the MRC NW-DMS DLA programme. Uazman Alam, Reader and Honorary Consultant in Diabetes and Endocrinology, is named in the announcement alongside Yalin Zheng, Shazli Azmi, Ram Kumar and Roberto Ferrero. The interdisciplinary setting connects engineering, medicine, clinical measurement, autonomic physiology and MedTech innovation.
The opportunity is suited to applicants interested in academic research, healthcare innovation, digital health, wearable sensing, clinical research or industry-based MedTech careers.
Funding details
The PhD opportunity is described as fully funded under the MRC NW-DMS DLA programme. No stipend amount or specific tuition-fee details are provided.
What's required
Applicants must hold or be about to obtain at least an Upper Second class UK honours degree, or equivalent international qualification, in biomedical engineering, electrical or electronic engineering, computer science, physics, applied mathematics, health data science or a closely related quantitative discipline. Research experience in biomedical signal processing, time- and frequency-domain analysis of physiological time series, or machine learning applied to sensor data is desirable. Programming proficiency in MATLAB and/or Python is desirable. No prior clinical experience is required, as training in clinical measurement, autonomic physiology and good clinical practice will be provided. Applicants should be interested in working at the interface of engineering, medicine and industry.
How to apply
Apply through the application link provided in the post and consult the additional details link for project and application information.
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Review drafts and confirm requirements with the university.
Professors
Reader and Honorary Consultant in Diabetes and Endocrinology
United Kingdom
Research Interests
Artificial Intelligence
Ml
Biomedical Engineering
Data Science
Medical Science
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.