Research Associate in Speech and Acoustic Sensing for Respiratory Health

Overview

We are seeking a postdoctoral research associate to join LungSight, 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.

LungSight 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.

Main duties and responsibilities

• 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 LungSight 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

Person Specification

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.

Criteria | Essential or desirable | Stage(s) assessed at

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 research experience in machine learning or deep learning, with practical experience of developing models in Python using a modern framework such as PyTorch. | Essential | Application/interview

Research experience in speech, audio, acoustic signal processing or closely related time-series analysis. | Essential | Application/interview

Knowledge of modern representation learning approaches, such as self-supervised learning, foundation models or Transformer-based architectures. | Essential | Application/interview

Ability to design rigorous experiments, analyse results critically, and evaluate model robustness, generalisability and potential confounding factors. | Essential | Application/interview

Evidence of the ability to communicate research effectively through peer-reviewed publications, technical reports, presentations or equivalent outputs. | Essential | Application/interview

Ability to work independently while contributing effectively to a multidisciplinary and multi-institutional research team, including close collaboration with researchers using complementary machine-learning and signal-processing approaches. | Essential | Application/interview

Strong organisational skills, including the ability to manage research tasks, code and data reproducibly and deliver work to agreed milestones. | Essential | Application/interview

Experience of multimodal machine learning, healthcare/biomedical AI, or analysis of respiratory/voice data. | Desirable | Application/interview

Experience working with sensitive data, secure data environments, clinical collaborators, NHS partners or responsible AI/data-governance processes. | Desirable | Application/interview

Further Information

Grade | Grade 7

Salary | £38,784 - £42,254

Work arrangement | Full-time

Duration | Two years

Line manager | Senior Lecturer in Medical Computing (project lead)

Direct reports | None

Right to work in the UK | If you do not currently hold the right to work in the UK, you can find more information here to help determine your visa eligibility. Additional guidance is also available on the UK Visa & Immigration website.

Our website | sheffield.ac.uk/cs

For informal enquiries about this job contact Dr Ning Ma, project lead, at N.Ma@sheffield.ac.uk

Next steps in the recruitment process

It is anticipated that the selection process will take place within a month of the job advert closing. This will consist of a formal interview. We plan to let candidates know if they have progressed to the selection stage within two weeks of the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact COM-Recruitment@sheffield.ac.uk

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What we offer

• A minimum of 41days annual leave, including bank holiday and closure days (pro rata) with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development access to learning and mentoring schemes, integrated with our Academic Career Pathways.
• A range of generous family-friendly policies
• paid time off for parenting and caring emergencies
• access to menopause support in the workplace
• paid time off and support for fertility treatment
• and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Leader (opens in a new window). If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.