Research Associate in Speech and Acoustic Sensing Respiratory Health

Research Associate in Speech and Acoustic Sensing for Respiratory Health

Posting

Start Date:

24/09/2026 Job  School/Department:
Computer Science Work Arrangement:
Full Time (Hybrid) Contract Type:
Fixed-term Salary per annum (£): £38,784 - £42,254 Closing Date: 01/11/2026

We are seeking a postdoctoral research associate to join Lung Sight, a multidisciplinary project developing low-cost, non-invasive audio-visual AI for earlier identification of chronic lung disease. The project aims to support a shift from reactive, clinic-based diagnosis towards proactive home screening using everyday devices such as smartphones, tablets and home computers.

Based in the School of Computer Science at the University of Sheffield, you will lead the machine learning research on acoustic foundation models for respiratory health. You will work with large-scale real-world recordings of speech, breathing and cough, developing self-supervised learning and foundation-model approaches that are robust to noise, demographic variation and different recording environments. You will also contribute to clinically informed acoustic biomarkers and to the integration of audio with visual and clinical information.

Lung Sight is a multi-university collaboration involving collaborators from the Manchester Metropolitan University, the University of Southampton, the University of Cambridge, the University of Leicester and the University of Leeds, alongside NHS and third-sector partners. This post will work particularly closely with a complementary PDRA at Southampton, whose research will focus more strongly on clinically informed acoustic feature engineering, while the Sheffield post has a stronger machine learning and representation learning focus.

The role offers opportunities for high-quality publications, open research outputs, clinical and stakeholder engagement, and career development at the interface of AI and healthcare.

• Develop and evaluate domain-adapted acoustic foundation models for respiratory health using speech, breathing and cough recordings.
• Curate, pre-process and analyse large-scale real-world audio datasets, including sensitive healthcare and helpline recordings processed within appropriate secure data environments.
• Investigate state-of-the-art self-supervised learning approaches, including Transformer-based architectures, masked prediction and contrastive learning, for robust respiratory acoustic representation learning.
• Fine-tune and validate models on labelled respiratory datasets and clinically relevant downstream tasks, assessing accuracy, generalisability and clinical utility.
• Investigate potential confounding factors, demographic bias, background-noise effects and domain shift, and develop methods that improve trustworthy and equitable model performance.
• Contribute to clinically informed acoustic modelling, working closely with the Southampton PDRA, who will lead complementary feature-engineering research; investigate how engineered physiological features can be combined with learned representations from the Sheffield machine-learning work.
• Collaborate with project researchers to develop multimodal approaches combining acoustic, visual and clinical information for respiratory disease screening.
• Work closely with researchers across the Lung Sight consortium, including Manchester Metropolitan University, Southampton, Cambridge, Leicester and Leeds, as well as clinical, NHS, patient/public and third-sector partners; participate in project meetings, joint research activities and dissemination.
• Lead and contribute to high-quality research publications and presentations, support project reporting, and contribute to the supervision and development of students where appropriate.
• Carry out other duties, commensurate with the grade and remit of the post

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

A PhD (or equivalent research experience) in computer science,electronic engineering,speech/audio processing,machine learning,biomedical engineering or a closely related discipline.

Essential

Application/interview

Strong…