PhD student (Wearable Mobility Sensing )

TUM CREATE LIMITED; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

TUM CREATE LIMITED in Singapore invites applications for a Research Associate / PhD candidate focusing on Wearable Mobility Sensing and Digital Biomarkers for Heart Failure Monitoring. The role is embedded in the HEARTwise team, collaborating with clinical cardiologists, engineers, and MedTech partners.

The candidate will work on actibelt-based mobility analysis, develop methods for inertial data, and contribute to translational clinical deployment within a multidisciplinary research setting at

Qualifications

• Master's degree required in Biomedical Engineering, Electrical Engineering, Computer Science, Data Science or Health Informatics.

Responsibilities

• Validate and adapt actibelt technology for HF patient monitoring in Singapore, extracting mobility parameters (real-world walking speed, stair-climbing ability, gait indices).
• Contribute to design and execution of a clinical pilot study (35 HF patients + reference cohort) at NHCS/NUHCS.
• Develop and evaluate a prototype clinical decision support system integrating actibelt-derived parameters with other digital inputs (e.g., digital stethoscope, digital scale).
• Publish findings in peer-reviewed journals and present at international conferences.
• Engage across the HEARTwise consortium and contribute to collaborative discussions.

Education

Master's degree in Biomedical Engineering, Electrical Engineering, Computer Science, Data Science, Health Informatics

Job description

About TUMCREATE

TUMCREATE is a multidisciplinary research platform of the Technical University of Munich (TUM) at the Singapore Campus for Research Excellence and Technological Enterprise (CREATE). We are partnering with universities, public agencies, and industry to advance future technologies.

The HEARTwise project is a public-private research initiative funded by Singapore's National Research Foundation, bringing together clinical cardiologists (CADENCE / NUHS, NHCS),engineers from TUM and TUMCREATE, and MedTech partners. The project develops a multimodal platform for non-invasive, early decompensation detection in ambulant HF patients - combining wearable mobility sensing, acoustic environment recognition, and integration with additional digital biomarkersources.

As a member of the HEARTwise team, you will collaborate within an interdisciplinary consortium comprising leading experts in cardiology, telemedicine, biomedical engineering, acoustics, data privacy, and industry.

Please visit www.tum-create.edu.sg for more information about TUMCREATE.

Job Title/Position

Research Associate / PhD candidate (Wearable Mobility Sensing and Digital Biomarkers for Heart Failure Monitoring)

Background & Scientific Context

Heart failure (HF) affects 2.5% of Singapore's population and is the leading cardiac cause of hospital admission -presenting a decade earlier and more aggressively than in Europe or North America. The 30% rehospitalization rate within 90 days of discharge is both a major clinical burden and a window of opportunity for early, data-driven intervention.

This is a fixed-term contract until March 2029 when applicable.

Supervision & Academic Environment

The position is jointly supervised by principal investigators at TUM Munich and TUMCREATE Singapore, with close integration into the CADENCE clinical network.

The candidate will work within a growing team of researchers across engineering and clinical disciplines in Singapore, with strong links to the Munich ecosystem.

The position combines the rigor of TUM's doctoral program with direct access to clinical partners and the unique research infrastructure of TUMCREATE.

The Research Position

This PhD is embedded in the HEARTwise engineering team at TUMCREATE Singapore and is centered on the actibelt®wearable technology platform - a body-worn 3D accelerometer system with a mature clinical track record, an established algorithm library, and over 64 years of recorded patient data.

The core scientific contribution expected is the development, validation, and clinical deployment of actibelt-based mobility analysis in HF patients.

The candidate will engage with open methodological questions - including how to extract richer clinical information from inertial data, how to improve reference data acquisition for algorithm development, and what mobility-based signatures may carry predictive value for HF decompensation - as well as with the translational challenge of deploying these methods in a real clinical setting.

Central research questions include:

Which mobility parameters derivable from belt-worn inertial sensing are most informative for detecting early functional deterioration in ambulant HF patients?

What methodological advances in reference data acquisition, sensor placement, or algorithm generalization can improve the quality and scalability of wearable-based gait analysis?

How can a prototype clinical decision support tool effectively integrate actibelt-derived mobility markers with additional digital inputs to inform clinical management?

Your Responsibilities

Core project deliverables:

• Validate and adapt actibelt® technology for HF patient monitoring in Singapore, extracting and evaluating mobility parameters (real-world walking speed, stair-climbing ability, gait quality indices)
• Contribute to the design and execution of a clinical pilot study (35 HF patients + reference cohort) at NHCS/NUHCS
• Contribute to a prototype clinical decision support system integrating actibelt-derived parameters with additional digital inputs (e.g., digital stethoscope, digital scale)
• Publish findings in peer-reviewed journals and present at international conferences
• Contribute to the CACOM lecture series(Clinical Applications of Computational Medicine, TUM)

Research and methodological development:

• Design and pursue original research questions within the project's scientific scope
• Develop and evaluate novel algorithmic approaches for inertial signal processing, activity classification, or clinical endpoint extraction
• Contribute to the broader actibelt algorithm development pipeline, including work on reference data collection methodology and algorithm validation frameworks
• Engage across the HEARTwise consortium and bring your own scientific perspective to collaborative discussions

Required Qualifications:

• Master's degree in Biomedical Engineering, Electrical Engineering, Computer Science, Data Science, Health Informatics,