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ETH Zurich is one of the leading universities of the world with a strong focus on science and engineering. In 2010 it established the Singapore-ETH Centre (SEC) in collaboration with the National Research Foundation (NRF) to do interdisciplinary research on pressing problems.
In collaboration with the National University of Singapore (NUS), the Nanyang Technological University (NTU), Duke - NUS, the National Health Group (NHG), National University Health System (NUHS), and SingHealth, SEC is currently undertaking a research program on "Future Health Technologies (FHT)”. FHT addresses fundamental health challenges by developing a future-oriented Mobile Digital Health Concept that tackles the increase in patients suffering from chronic diseases such as diabetes, osteoporosis, obesity, stroke, but also those susceptible to injurious falling, as a consequence of a rapidly ageing population. Within FHT via the use of mobile digital technologies we want to tackle these injuries and illnesses covering the value chain comprehensively from acute care to patient's private homes. Within the broader FHT framework we are announcing the following job opening.
Our Module within FHT addresses can be effectively targeted. The aim of the umbrella Project, placed within our Module, is to provide an assessment of fall risk in a personalised manner via the use of wearable technology. The approach combines the state-of-the-art multipoint wearable sensor systems () with comprehensive neuromuscular model for movement ().
Most injurious falls occur during walking, not surprising as it is the most common activity of daily living. In order to walk effectively, we need intricate coordination of our limbs both spatially and temporally, for maintaining balance in a continuous manner. Age-related decline poses challenges in being able to walk and this burden is further intensified by the individual’s susceptibility to injurious falling. As part of this PhD project, we aim to establish a distribution of gait parameters (i.e. stride time and length, dual limb support time etc.) in a comprehensive and personalised manner. Such characterisation will allow us to address age-related decline in task performance and its association with injurious falling.
The general concept is to develop a comprehensive understanding on movement patterns collected via wearables during walking with an aim to enhance clinical uptake. Gait data from multiple sensors will be collected, while the elderly individuals walk for a short period of time. In addition, we will also tap into clinical questionnaires, including elderly individuals’ fall history, psychosocial status, as well as cognitive ability, among others. This hybrid clinical battery of data will be collected from a large cohort of elderly individuals. The primary task will be to process, and analyse wearable-based walking datasets such that we are able to identify and report gait cycles, spatio-temporal parameters and movement patterns. These gait parameters and patterns will then be used for assessment of fall risk using statistics and modelling based approaches. A critical aspect is the ability to benchmark these gait parameters in order to allow an accurate estimation of individual’s fall risk. Such benchmarking will also help in targeting intervention for risk mitigation.
For the purposes of the project, Singapore is an ideal choice. It’s population is highly tech-savvy, its healthcare system is clearly structured. Critically, Singapore is facing (and will continue to face) one of the largest increases in the proportion of elderly in its population. It is likely that Singapore will rank among the top 10 “oldest countries” worldwide. Singapore also happens to be one of the best places to live in Asia. The reasons are many, but primary factors are efficient public transport, and education systems and substantial health care industry.
Please note that the employment will be at the Singapore-ETH Centre and local working regulations will apply. Workplace is Singapore. Please visit: for details. The duration of employment is 3 years.
A Master’s degree in biomedical, electronics, electrical, mechatronics and other engineering fields, or physics and neuroscience. Considerable experience and understanding in working and handling data collected with wearable technologies, especially inertial measurement units (IMUs). Experience in aspects of signal processing including filtering, synchronising datasets collected from different modalities, etc. is critical. Basic understanding of multivariate statistics especially in relation to bioengineering applications is desired.
Programming skills: Considerable experience/expertise in Python and/or R (but do possess a basic understanding of Matlab). Previous experience with movement datasets 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 biomechanists, engineers, computer scientists as well as health-care and clinical scientists? Do you have a penchant for collaborating - maintaining channels of communication - with lab/team members in LMB Switzerland, but also worldwide? If yes, this job might just be for you.
We look forward to receiving your online application with the following documents:
Questions regarding the position should be directed to , PhD, email firstname.lastname@example.org (no applications).
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