The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world.

For more details, please view https://www.ntu.edu.sg/mae/research.

We are looking for a Research Engineer, Embedded ML to join the MetaSense project — a wearable cardiometabolic sensing platform built on patent-pending dual-point photoplethysmography (2PPG) and ECG. This is a 1-year, closely supervised role, spanning embedded hardware, software, and biocomputational AI/ML development for continuous, non-invasive cardiometabolic monitoring.

About MetaSense Project

MetaSense is a cuffless cardiometabolic wearable platform under development at NTU's School of Mechanical & Aerospace Engineering, in close collaboration with the National Heart Centre Singapore (NHCS/SingHealth) and A*STAR. Built around a patented dual point photoplethysmography (2PPG) modality, the project seeks to develop wearable systems and AI learning models for continuous hemodynamic biomarkers. MetaSense is a biooptics platform for continuous Cardiometabolic monitoring by replacing episodic measurements with continuous, non-invasive Al-powered biomarker tracking. Patent:  https://patentscope.wipo.int/search/en/WO2024205510

Why Join MetaSense?

• Exposure to embedded firmware and resource-constrained machine learning for implementation in wearable biosensing.

• Exposure to how physiological ML models are trained, validated for clinical applications.

• Exposure to the translational path from academic research toward regulatory-grade cuffless blood pressure wearables (e.g. HSA/FDA pathways).

• Direct, hands-on mentorship from the project's Research Fellow and Principal Investigator.

• Real translational research experience: contribute to a patent-pending wearable cardiometabolic platform working toward clinical validation, alongside NHCS/SingHealth and A*STAR.

Key Responsibilities:

You will support biocomputational AI/ML development, assist with embedded firmware and hardware bring-up for wearable PPG/ECG sensors, help prepare and validate physiological datasets, and support dashboard and visualisation tools — all under close guidance from the project’s Research Fellow and Principal Investigator.

• Support biocomputational AI/ML development — signal processing, pulse waveform morphology, and hemodynamic feature extraction — for cardiometabolic disease applications, under close PI/Co-I guidance.

• Assist with embedded firmware and hardware bring-up for wearable PPG/ECG sensor front-end, including microcontroller integration and sensor interfacing (I²C/SPI).

• Assist in preparing, cleaning, and validating physiological datasets (clinical and public) for model training and evaluation.

• Support development of dashboards and visualization tools for live sensor data and model outputs.

• Maintain clear technical documentation and take part in regular discussions with collaborators across NTU, NHCS/SingHealth, and A*STAR.

• Take on ad hoc technical tasks as directed by the Principal Investigator or project supervisors.

Job Requirements:

• Bachelor’s or Master’s degree in Computer Science, Electrical & Electronic Engineering, Mechanical & Aerospace Engineering, or a related field

• Confident proficiency in Python; familiarity with embedded electronics (microcontrollers, I²C/SPI/UART) and Git

• Comfortable working across hardware, software, and data domains

• Self-motivated and a good communicator, comfortable in a small, closely supervised team.

We regret to inform you that only shortlisted candidates will be notified.