Research Associate in Speech and Acoustic Sensing for Respiratory Health

Dunhillmedical; Sheffield; Hybrid; GBP 39,000 - 42,000; Full time

Benefits offered by this job

Hybrid working

Generous pension scheme

Flexible working opportunities

Job summary

The University of Sheffield is seeking a Research Associate in Speech and Acoustic Sensing for Respiratory Health to advance acoustic foundation models for chronic lung disease screening. You will work within LungSight, leading ML research on audio data and collaborating with clinical partners in a multi-university setting.

You will develop self-supervised learning techniques, handle large real-world datasets, and contribute to multidisciplinary efforts at the interface of AI and healthcare,

Qualifications

• PhD (or equivalent) in computer science, electronic engineering, speech/audio processing, machine learning, biomedical engineering or closely related field.

Responsibilities

• Develop and evaluate domain-adapted acoustic foundation models for respiratory health using speech, breathing and cough recordings.
• Curate and analyse large-scale real-world audio datasets in secure environments.
• Investigate self-supervised learning, transformers and robust representation learning for respiratory data.
• Fine-tune models on labelled datasets and assess accuracy, generalisability and clinical utility.
• Collaborate with multi-institutional researchers to create multimodal approaches combining acoustic, visual and clinical data.
• Lead research publications and dissemination, supervise students where appropriate.

Skills

Machine learning

Deep learning

Experiment design

Multimodal ML

Research communication

Independent work

Education

PhD in computer science / ML / speech processing

Tools

PyTorch

Python

Job description

Research Associate in Speech and Acoustic Sensing for Respiratory Health

Posting Start Date: 24/09/2026 Job Id: 3040 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

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

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.

Further Information

Grade 7

£38,784 - £42,254

Full-time

Two years

Senior Lecturer in Medical Computing (project lead)

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 [e-mail]

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 [e-mail]

Our vision and strategic plan

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

We are a Disability Confident Leader

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 recruitment process.

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision ( www.sheffield.ac.uk/vision ) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.

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[phone]

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