Research Associate: AI for Respiratory Health (Speech)

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

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,