Sponsored PhD Position - Bioacoustic Signal Processing and Machine Learning

About the Programme

This position is offered under the Singapore Economic Development Board (EDB) Industrial Postgraduate Programme (IPP), a national initiative that integrates academic research with industrial application through joint doctoral training. The PhD candidate will join the Cough Analysis Learning Model (CALM) research programme, a strategic collaboration between Nanyang Technological University (NTU), Denka, and Aevice Health. CALM aims to advance bioacoustic science and artificial intelligence to enable reliable analysis of cough patterns for respiratory health monitoring, disease differentiation, and digital biomarker discovery.

About the Partners

Nanyang Technological University (NTU) is a leading research-intensive university ranked #12 in the world (QS World University Rankings 2026). Its School of Electrical and Electronic Engineering (EEE) is ranked #4 globally for Electrical and Electronic Engineering and is internationally recognised for excellence in signal processing, communications, and artificial intelligence.

Denka Company Limited is a global chemical and healthcare company headquartered in Tokyo and listed on the Tokyo Stock Exchange (TSE: 4061). Founded in 1915, Denka's core businesses span advanced performance materials, infrastructure solutions, and life innovation, including diagnostics and biotechnology. The company is a key manufacturer of vaccines, diagnostic reagents, and specialty chemical products supporting healthcare and sustainability worldwide. Denka Life Innovation Research Pte. Ltd. (DLIR) serves as a regional Research hub driving next-generation diagnostic and healthcare solutions.

Aevice Health is a Singapore MedTech company pioneering wearable technologies and digital health platforms for continuous respiratory monitoring. Its innovations support early detection and personalised management of respiratory conditions. Recognised globally, Aevice Health was featured in the Forbes Asia 100 to Watch (2025) list and has received multiple awards, including the CES Best of Innovation in Digital Health (2023), OCBC Emerging Enterprise Award (2018), and IEEE N3XT Star Award (2018). Its technology is approved by the Singapore HSA and cleared by the U.S. FDA.

Research Focus

The PhD candidate will develop bioacoustic signal processing and machine learning algorithms for cough detection, segmentation, classification, and disease modality prediction using data from wearable and environmental acoustic systems. The research integrates advanced digital signal processing, and interpretable AI to identify unique cough signatures associated with various respiratory disease modalities such as asthma, COPD, and respiratory infections.

Key research domains include:

. Cough event detection and feature extraction from audio recordings

. Development of interpretable AI models for cough classification and disease prediction

. Cross-domain model generalisation across subjects, sensors, and environments

. Integration of algorithms into embedded or cloud-based systems

Responsibilities

. Conduct PhD-level research in bioacoustic signal processing and AI for respiratory acoustics

. Develop, train, and validate models for cough detection, classification, and disease prediction

. Collaborate closely with Aevice Health engineers and Denka Research scientists

. Publish findings in high-impact journals and international conferences

. Contribute to intellectual property creation and translational technology development

. Support integration of research outputs into Aevice Health's wearable stethoscope platform

Requirements

. Bachelor's or Master's degree in Electrical & Electronic Engineering, Physics, Data Science, Computer Engineering, or Biomedical Engineering

. Strong foundation in signal processing, machine learning, or acoustics

. Proficiency in Python for data analysis and model development experience with MATLAB for signal processing and algorithm prototyping will be an advantage.

. Excellent written and verbal communication abilities

. Collaborative mindset with the ability to work effectively across academic and industrial teams

. Must meet the entry requirements for PhD candidature at NTU's School of Electrical and Electronic Engineering [Link]

Application Process

Interested applicants should submit the following to [Confidential Information] (CC: [HIDDEN TEXT])

1. Curriculum Vitae

2. Academic transcripts (Bachelor's and/or Master's)

3. Contact details of two referees

We regret that only shortlisted candidates will be notified.