PhD Student in Digital Health Technologies and Data Mining for Neurorehabilitation

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100%, Zurich

The Rehabilitation Engineering Lab (RELab) at the Department of Health Sciences and Technology at ETH Zurich uses robotics, wearable sensor technologies and non-invasive neuroimaging to explore, assess and restore sensorimotor function in persons with neurological injury, with the goal of promoting independence. We closely collaborate with neurorehabilitation clinics and clinical scientists to develop and clinically evaluate our technologies.

The RELab is located at Balgrist Campus, a research campus next to the Balgrist University Hospital and its Spinal Cord Injury Center, which brings together researchers from Balgrist University Hospital, University of Zurich, Children's Hospital Zurich and ETH Zurich.

Project background

The RELab has a strong focus on developing, validating, and clinically applying technologies for assessing sensorimotor impairments in neurological disorders, with a focus on the upper limb. These technologies are expected to allow a fine-grained description of upper limb impairments, which will allow to provide sensitive outcome measures for clinical studies and novel insights into disease mechanisms.

This project relies on a previously established technology-based assessment, the Virtual Peg Insertion Test (VPIT ), which allows to characterize upper limb movement patterns and hand grip forces using a standardized task based on a robotic end-effector device. From this task, a set of validated sensor-based metrics were derived, which describe, for example, movement smoothness and grip force control. While this information already complements data that can be extracted from standard clinical tests, it is still a major research question how to use these novel metrics to influence and improve clinical decision making.

The aim of this project is to further optimize the VPIT (concept, data processing pipeline, statistical analysis) and clinically apply it in different neurological populations. This project is done in close collaboration with our clinical partners, for example, at the Kantonsspital Baden and the Kliniken Valens. The project relies on previously collected data (from over 300 neurological patients), but also requires to conduct clinical studies.

Job description

Your tasks will include:

• Get familiar to the VPIT and the neurophysiological reasoning behind the already implemented metrics and the evidence collected so far in our previous studies.
• Optimize the existing set of metrics for different neurological disorders and explore their application in new target populations (e.g., Parkinson’s disease)
• Rely on the VPIT metrics to characterize the longitudinal evolution of sensorimotor impairments in neurological disorders
• Establish and validate advanced computational models based on artificial intelligence/machine learning that can help predict neurorehabilitation outcomes based on clinical data and the VPIT metrics
• Integrate these computational models into clinical decision making in an explorative clinical study
• Coordinate the conduction of clinical studies, including collecting multi-modal behavioral data collection and interacting with clinical partners
• Communication of research outcomes at international conferences and in peer-reviewed journals

We are looking for a candidate that can start as soon as possible.

Your profile

You will have:

• A MSc in Biomedical Engineering, Computer Science, Neuroinformatics, or related fields
• A strong interest in working with and developing digital health technologies for neurorehabilitation
• Extensive experience with advanced signal processing and data mining techniques for analyzing biological signals (biomarkers)
• Strong knowledge in statistics, artificial intelligence, machine learning and computational modeling
• Motivation to work with clinicians and with patients (data collection)
• Experience with the development of technologies for assessing impaired body functions, including hardware development/maintenance
• Knowledge of important considerations for obtaining high quality biological data in challenging environments
• Strong verbal and written communication skills in German and English

Scientific curiosity and motivation to perform scientifically rigorous experimental work

ETH Zurich

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.

Working, teaching and research at ETH Zurich