Postdoc Researcher (2 Years) - T150-01201 BE-FIT SEC

Singapore ETH-Centre; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading research institution in Singapore is seeking a researcher to investigate the movement patterns of older adults using wearable sensors. The role involves developing machine learning models to analyze gait data and assess fall risk. Candidates should have a PhD in a relevant field and at least 2 years of experience in machine learning, particularly in healthcare applications. This position offers a multidisciplinary environment collaborating with experts in various fields.

Benefits

Equal opportunity employer

Multicultural work environment

Focus on work-life balance

Qualifications

• Minimum 2 years of experience in machine or deep learning.
• Expertise in predictive model development for healthcare applications.
• Strong foundation in machine learning algorithms and study design.

Responsibilities

• Investigate movement patterns among older adults.
• Acquire movement data using wearable sensors.
• Generate machine learning models to assess fall risk.

Skills

Machine learning

Deep learning

Statistical analysis

Experimental design

Feature extraction

Communication

Education

PhD in computer science, computer vision, neuroscience, physics, or biomedical engineering

Tools

Python

PyTorch

Matlab

R

Tensorflow

Job description

Job description

Job description

We will investigate movement patterns and features of walking outdoors and in the neighbourhoods among vulnerable older adults (suffering from OA as well as at high risk of falling) in order to understand perceptions on interacting with built environment. We will acquire these movement patterns and features using the state‑of‑the‑art inertial measurement units (wearables such as ZurichMOVE or Axivity) sensors. These sensors are equipped with triaxial accelerometers and gyrospcopes and provide assessment of aspects such as impact and swing behaviour during different movements. Specifically we will be addressing the following research questions:

• What are the kinematic characteristics of walking among older adults with OA and / or previous falls under ecological settings (neighbourhoods)?
• Do the kinematic characteristics of naturalistic walking predict physical activity rates?
• Does the design of walkways (including overall layout, e.g. design of curbs, pathways etc and accessibility features e.g. size of the curbs, or height of side walk, ramps vs stairs) impact overall levels of physical activity as well as specifics of walking quality?

The general concept is to generate machine learning as well as statistics‑based models to extract movement patterns from movement / locomotion / walking / gait dataset collected via wearable sensors. The gait data from multiple sensors will be collected while participants (older adults) move / walk / transition for both short (up to 10 minutes) as well as long (over multiple days) periods of time. This data will be complemented with data from our collaborators on walkability assessment, where we evaluate naturalistic observation on individual’s daily routes and outdoor activities. Finally, we will also tap into questionnaire data involving individuals’ fall history, psychosocial status as well as cognitive ability, as well as their perception of the built environment. The primary task will be to extract features (gait signatures, but also artificial “machine learned” features) that allow us to assess fall risk in an individualized manner. Crucial aspects are the interpretability and repeatability of these signatures as these aspects will allow clinical as well as stakeholder uptake. Another important aspect for uptake is the association (via analysis as well as interpretation) of these features to the walkability as well as clinical assessment to provide a hybrid ‘mapping’ of the manner in which older adults interact with their environment.

Profile

Minimum 2 years of experience in machine or deep learning with a background (PhD) in computer science, computer vision, neuroscience, physics or biomedical and / or other engineering fields. Expertise in predictive model development, especially for healthcare applications. A solid understanding in experimental design, feature extraction, selection, and analysis, as well as tailoring machine / deep learning techniques to hybrid datasets including clinical battery, and objective physiological (movement) datasets. A strong foundation in deep and machine learning algorithms, statistical analysis, and study design from ideation to evaluation and validation. A strong publication record especially in area of artificial intelligence and machine learning. Presentations at conferences and participation in workshops is desired.

Programming skills

• Expert in Python (PyTorch as well as the use of libraries).
• Experience / expertise in working with Matlab and / or R (or any other statistical software e.g. SAS).
• Previous experience with physiological (such as heart rate via an ECG or also EEG) datasets is desired, but not a must‑have.
• A solid foundation of machine learning frameworks such as Tensorflow and algorithms, statistical analysis, and study design from ideation to evaluation and validation.
• Experience with GUI development is desired.

Personal

• Are you motivated to work on challenging problems?
• Can you work independently on a project level demonstrating problem solving skills?
• Do you see yourself fitting in with the team of multinational group of biomechamists, engineers as well as health‑care and clinical scientists?
• Do you have a penchant for collaborating - maintaining channels of communication - with lab / team members but also worldwide? If yes, this job might just be for you.

Workplace

Workplace

We offer

The Singapore-ETH-Centre is an equal opportunity and family‑friendly employer. All candidates will be evaluated on their merits and qualifications, without regards to gender, race, age or religion. The employment will be at the Singapore-ETH Centre and local working regulations will apply.

We value diversity

In line with to find out how we ensure a fair and open environment that allows everyone to grow and flourish.

Curious? So are we.

We look forward to receiving your online application with the following documents:

• A cover letter outlining your motivation and experience in the field of machine-learning and bioengineering with a focus towards practical applications in healthcare
• Curriculum Vitae or Resume
• List of publications and abstracts of presentations at conferences
• Certificates (e.g. PhD and Master’s degree)
• Transcripts of records

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about BE-FIT Project can be found on our website and Questions regarding the position should be directed to Dr. Navrag Singh email : and Mr. Aaron Ang email : .

Singapore-ETH Centre

The Singapore-ETH Centre provides a multicultural and interdisciplinary environment to researchers working on diverse themes focussed on sustainable and liveable cities, resilient urban systems, and patient‑centric healthcare. The centre is home to a community of over doctoral, postdoctoral and professorial researchers working in three main programmes : Future Cities Laboratory, Future Resilient Systems, and Future Health Technologies.