Overview

This project aims to address the critical gap between the huge potential offered by wearable sensors and their still very limited adoption in clinical practice. This will be achieved by creating scientifically rigorous data processing pipelines that provide users with clear and interpretable information about data accuracy and uncertainty.

About this Opportunity

This project builds on an 8-year research collaboration between the Primary Supervisor and the Partner, aimed at increasing the clinical adoption of wearable technology, through the development of scientifically rigorous software tools that provide clinicians with clear and interpretable information about the accuracy and reliability of data collected by wearable sensors.

The uniqueness of this team's research direction is the focus on a scientifically rigorous and interpretable uncertainty quantification of wearable data, which is missing from the vast majority of studies, especially those using machine learning techniques. An accurate uncertainty quantification is of critical importance for two main reasons:

• to allow correctly fusing information from multiple sensors, and
• to allow clinicians or other users to make informed decisions based on the measured data, by distinguishing between actual changes in relevant physiological indicators and artifacts caused by noise/disturbance or signal processing errors.

Objectives

In this project, the candidate will build on the existing research, with the following key objectives:

1. Expand the work done on PPG signals, considering also other commonly measured signals (accelerometry, electrodermal activity, and possibly others), to improve the measurement accuracy and uncertainty quantification by fusing information from all those signals.
2. Combine the already-developed Taylor-Fourier analysis with other tools suited for uncertainty quantification (e.g., Gaussian processes) and possibly machine learning techniques, if appropriate.
3. Implement the developed algorithms in a device-agnostic software, allowing clinicians to seamlessly analyse and combine data recorded from a variety of different wearable devices.

Eligibility

Candidates will have, or be due to obtain, a Master's Degree or equivalent in a relevant subject. Exceptional candidates with a First Class Bachelor's Degree in an appropriate field or significant relevant experience will also be considered.

Funding

This UKRI funded Studentship will cover full tuition fees and pay a maintenance grant for 3.5 years, at the UKRI standard rates. The Studentship also comes with access to additional funding in the form of a Research Training Support Grant to fund consumables, conference attendance, etc.

Application Process

To apply, candidates should:

1. Contact supervisors to discuss the project.
2. Prepare application documents, including:

• A research proposal
• University transcripts and degree certificates
• Passport details
• English language certificates (international applicants only)
• A personal statement
• A curriculum vitae (CV)
• Contact details for two proposed supervisors
• Names and contact details of two referees.

3. Apply online, including the project title and reference number ENGDLA001.

Research Environment

The student will be based at the University of Liverpool but will work closely with the Partner throughout the duration of the project. Opportunities for other collaborations may also be available.

Study Details

• Reference number: ENGDLA001
• Funding: Funded
• Study mode: Full-time
• Apply by: 19 April 2026
• Start date: 1 October 2026
• Subject area: Electrical Engineering and Electronics

University of Liverpool