Transforming Global Health: AI-Driven Acoustic Biomarkers for Early Lung Disease Detection (Ref: FP-SA26-MK)

Full Description

Harness the power of Artificial Intelligence to address a major global health crisis.

Respiratory illnesses remain a leading cause of death worldwide, and we are offering an exceptional PhD opportunity to contribute to solving this challenge.

The Challenge & Our Approach:

Many serious respiratory conditions, including COPD and asthma, originate in the small airways – often called the “silent zone” because early abnormalities are missed by current diagnostic methods.

This project sits at the cutting edge of biomedical engineering, AI, and respiratory medicine, developing a non-invasive, low-cost diagnostic tool by analysing subtle acoustic signals generated within diseased lungs.

Your Role & Research Training:

You will join a highly supportive supervisory team with world-leading expertise in computational lung modelling.

Working with our proprietary respiratory system model, you will generate large-scale, high-quality virtual airflow and acoustic datasets.

You will then develop and train state-of-the-art deep learning algorithms (e.g., CNNs, RNNs) to identify characteristic signatures of early airway disease.

This project is designed for real-world impact.

Through established clinical and industry collaborations, you will have the opportunity to embed your validated AI models directly into digital stethoscope platforms and support their translation into healthcare practice.

Who We Are Looking For:

We welcome motivated students with a strong background in engineering or science, such as physics, mathematics, computer science, or related disciplines.

Skills in mathematics, computational modelling, programming, signal analysis or machine learning are particularly valuable.

If you are keen to apply technology to improve global healthcare, we would be delighted to hear from you.

Name of primary supervisor/CDT lead:

Dr Minsuok Kim m.kim@lboro.ac.uk

https://www.lboro.ac.uk/schools/meme/staff/minsuok-kim/

Name of secondary supervisor:

Dr Raman Maiti

https://www.lboro.ac.uk/schools/meme/staff/raman-maiti/

Entry requirements:

Applicants should have or expect to achieve, at least a 2:1 honours degree (or equivalent) in STEM or a related subject area.

A relevant Master’s degree and/or experience in machine learning will be an advantage.

English language requirements:

Applicants must meet the minimum English language requirements.

Funding information:

The studentship is for 3 years and provides a minimum tax-free stipend of £ 20,780 per annum (2025/26 rate) for the duration of the studentship plus university tuition fees.

Funding will be awarded on a competitive basis and is not guaranteed; availability will depend on the outcome of the selection process and subject to final approval by the University.

The following selection criteria will be used by academic schools to help them make a decision on your application: https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/studentship-assessment-criteria/.

Bench fees required: No

Closing date of advert: 17 February 2026

Start date: October 2026

Full-time/part-time availability: Full-time 3 years

Who is eligible to apply?: Both UK and International

How to Apply:

All applications should be made online.

Under programme name, select Mechanical and Manufacturing Engineering.

Please quote the advertised reference number: FP-SA26-MK in your application.

Applications must include a personal statement, up-to-date curriculum vitae (CV), details of two referees (one from your highest degree qualification), certified certificates and transcripts for all completed degree programmes, and a reference to the project FP-SA26-MK.

Submission of a Research Proposal is not essential but may strengthen your application.

Incomplete applications received after the closing date may not be considered for interview.

Shortlisted candidates will be contacted for an interview, which are expected in February/early March 2026.

Project search terms:

acoustics, artificial intelligence, biomedical engineering, computer science, fluid mechanics, machine learning, mathematical modelling, mechanical engineering, medical physics, medicine, computational modelling, respiratory dynamics

Email address Wolfson:

ws.phdadmin@lboro.ac.uk