ETH Zurich

Postdoc Researcher in Future Health Technologies on Assessing fall risk in elderly

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Postdoc Researcher in Future Health Technologies on Assessing fall risk in elderly

ETH Zurich is one of the world's leading universities 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 undertaking a research program on "Future Health Technologies FHT". In the Early Detection of Health Risks and Prevention research module, we use falls and fractures as a clinical test case due to the high incidence of injurious falls in ageing societies. We develop a holistic concept for large-scale screening that is specific and accurate in identifying individuals with an elevated risk of falling. For these high-risk individuals, the benefits of interventions designed for reducing fall risks at the community level are extremely high.

Project background

The global population is rapidly ageing, with the percentage of the population aged 60 years and older predicted to increase dramatically by 2050. Falls and fractures are major health risks to the elderly and preventing them is inherently linked with accurately identifying individuals at risk – those with motor deficits and compromised bone integrity. We propose that an underlying reason for this lack of substantial progress is that fall and fracture risks are approached independently, which ignores the fact that falls and fractures have overlapping aetiologies as well as a synergistic relationship. Our Module within FHT addresses early detection of risks, such that therapies aimed at reducing or preventing injurious falls can be most effective. The aim of the umbrella Project, placed within the Module on early detection of risks, 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 (ZurichMOVE) with comprehensive neuromuscular model for movement (cNeMo). Walking is one of the most common activities of daily living (ADL). Incidentally, most injurious falls occur during walking. Recent studies show that walking in an effective manner requires coordination of our limbs both spatially and temporally, such that we are able to maintain our balance in a continuous manner. This coordination involves intricate neuromuscular feedback. Age-related decline poses challenges in being able to walk effectively and this burden is further intensified by the individual’s susceptibility to injurious falling. As part of this project, we aim to establish a distribution of gait signatures or features (i.e. coordination, dynamic balance, etc.) during walking in a comprehensive manner for all individuals. Such characterisation will allow us to directly address the age-related decline in task performance and its association with injurious falling.

For the purposes of the project, Singapore is an ideal choice. Its population is highly tech-savvy, its healthcare system is clearly structured and there is a critical mass of accessible patients. Currently, Singapore is facing 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” together with other Asian and European nations. Singapore also happens to be one of the best places to live in Asia. HSBC’s annual survey rates it as the best city in the world to live for ex-pats, while Mercer’s rates it to have the best quality of life in Asia. There are many reasons, but primary factors are efficient public transport, education systems, and a substantial healthcare industry. It is also a very clean and safe city.

Job description

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 individualised manner. Crucial aspects are the interpretability and repeatability of these signatures as these aspects will allow clinical uptake, but also form the starting point for the intervention trial aimed at mitigating fall risk. Another important aspect of clinical uptake is the association (via analysis as well as interpretation) of these features to the clinically established gait parameters such as e.g. walking speed, cadence, and even joint angles.

Your profile

You will have:

  • A PhD in computer science, computer vision, neuroscience or engineering fields.
  • Considerable experience in machine learning as well as 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 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.
  • Programming skills:
    • Expert in Python (the use of its libraries).
    • Considerable experience/ expertise in working with Matlab and R (or any other statistical software e.g. SAS).
    • A solid foundation of machine learning algorithms, statistical analysis, and study design from ideation to evaluation and validation.

The following competence will be advantageous:

  • Previous experience in presenting at conferences and participation in workshops is desired.
  • Previous experience with movement (or other physiological metrics such as heart rate via an ECG or also EEG) datasets is desired, but not a must-have.
  • 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 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 Laboratory of Movement Biomechanics in Switzerland, but also worldwide? If yes, this job might just be for you.

We offer

  • a PostDoc position that allows you to contribute to one of the contemporary health challenges of our aging society
  • working on large scale dataset collected in the field to address an ongoing problem
  • an interdisciplinary team of passionate researchers working at the intersection of digital health technology and disease prevention
  • exciting professional development opportunities
  • access to a global network of digital health enthusiasts
  • opportunities to present your research to local and international audiences
Working, teaching and research at ETH Zurich

We value diversity

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website 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 including the following documents:

  • Cover letter outlining your motivation and experience in the field
  • CV including certificates (e.g. PhD and/or Master's degree)
  • List of publications and abstracts of presentations at conferences.
  • Transcript 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.

For further information about our research and projects, please visit our website. More information about the Early Detection of Health Risk and prevention Module of the FHT programme is available here: Questions regarding the position should be directed to Dr Navrag Singh at ( (no applications).

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

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Postdoc Researcher in Future Health Technologies on Assessing fall risk in elderly
Rämistrasse 101 Zürich, Zwitserland
Uiterste sollicitatiedatum
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