Research Associate in Speech and Acoustic Sensing Respiratory Health

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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…