Studierende finden an der ETH Zürich ein Umfeld, das eigenständiges Denken fördert, Forschende ein Klima, das zu Spitzenleistungen inspiriert.
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 with mobile digital technologies, covering the value chain 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 injurious falls can be most effective. 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 PostDoc project, we aim to establish a distribution of gait signatures (i.e. coordination, dynamic balance, 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 generate machine learning as well as statistics-based models to extract movement patterns from walking/gait dataset collected via wearable sensors. 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 extract features (gait signatures) that allow us to assess fall risk in an individualised manner. As interpretability and repeatability of these signatures will enhance clinical uptake, these are critical for the project end goal. These gait signatures, also form the starting point for the intervention trial aimed at mitigating fall risk within our module. Another important aspect for clinical uptake is the association of these signatures to the clinically established gait parameters such as e.g. walking speed, cadence, but also spatio-temporal parameters and even joint angles.
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 2 years. The start date is 15.10.2020.
A PhD in biomedical and other engineering fields, computer science, computer vision, neuroscience, or physics. Considerable experience in applying machine/deep learning techniques on hybrid datasets e.g. questionnaires vs. Objective datasets. Experience in aspects of experimental design, feature selection and extraction, and estimation of risk is critical. Basic understanding of multivariate statistics especially in relation to bioengineering applications is desired. Experience in supervision of Masters or Bachelor level students 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, but not necessary.
Personal: Are you highly 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 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:
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
Questions regarding the position should be directed to , PhD, email email@example.com (no applications).
Bitte sage uns wo du ähnliche Stellenanzeigen suchst und vergiss nicht deine E-Mail Adresse anzugeben!
Du möchtest dich mit nur einem Klick ganz einfach bewerben und immer auf dem neuesten Stand bezüglich neuer Stellenangebote, die zu dir passen, sein? Melde dich jetzt als Student an!Kostenlos anmelden