Research Scientist (1-year contract), SCIS, Singapore

Description

Responsibilities

Design and develop wearable hardware prototypes, including novel form factors, for reliable PPG sensing and on-device heart rate estimation under real-world conditions.
Develop signal processing and machine learning algorithms for robust physiological parameter estimation using PPG and auxiliary sensing modalities.
Design and implement resource‑efficient software pipelines for edge/on-device physiological monitoring under hardware and energy constraints.
Conduct large‑scale user studies and data collection across participants with varying demographics, physiological conditions, and activity levels.
Publish in top‑tier academic venues (SCIS A*/A venues).
Other duties as assigned.

Qualifications

Minimum PhD degree in Computer Science, Biomedical Engineering, Information Systems or closely related disciplines.
Minimum 2 years of relevant research experience in wearable‑based sensing, PPG signal analysis, embedded device, or signal processing.
Excellent academic record with publications in CS ranking A* venues (SenSys, PerCom, MobiSys, MobiCom, IMWUT).
Experience with the deployment and use of Deep Learning frameworks (e.g., TensorFlow or PyTorch), including the design of neural networks‑based models for activity recognition.
Proficiency in programming software/languages such as Python and C is required.
Experience with sensing using wearable devices, especially earables, is required.
Practical experience with resource‑efficient algorithms for human activity recognition and domain adaptation is highly desired.
Demonstrated experience and know‑how of signal processing is a strong plus.
Good analytical, technical and problem‑solving skills.
Research background in Human‑Computer Interaction is preferred.
Self‑motivated individual who can work independently and collaboratively with team members.

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Highlights

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