Postdoctoral Researcher in ML-based Diseases Modelling for Long-term Health Monitoring

Updated: 4 months ago
Job Type: FullTime
Deadline: 05 Jul 2023

6 Jun 2023
Job Information

ETH Zürich
Research Field

Computer science » Other
Engineering » Other
Medical sciences » Other
Computer science » Programming
Mathematics » Statistics
Researcher Profile

Recognised Researcher (R2)

Application Deadline

5 Jul 2023 - 21:59 (UTC)
Type of Contract

Job Status

Hours Per Week

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?


Offer Description

Postdoctoral Researcher in ML-based Diseases Modelling for Long-term Health Monitoring

Are you a highly motivated and enthusiastic researcher or engineer looking to make a difference in the field of AI for Healthcare? Join us at the Spinal Cord Injury Artificial Intelligence - SCAI Lab  at ETH Zurich.

Our team of clinical and research scientists is dedicated to improving the long-term prognosis of Spinal Cord Injury (SCI) through new healthcare systems using physiological and clinical information analysis for a closed-loop decision support system. 

This position is open for a postdoctoral researcher in the field of transparent machine learning and graphical modelling for health monitoring through wearable and remote sensing. The focus is on personalized healthcare monitoring and disease tracking such as cardiovascular diseases. Apart from actively shaping our group's research, the positions include international collaboration with the Japanese Moonshot project and clinical partners in Japan with academia and industry, mentoring MSc, and PhD students.

Dr Diego Paez-Granados in collaboration with Prof. Robert Riener (SMS Lab ) will supervise the successful researcher. 

The position is fully funded for 2 years. Ideal starting date: August 2023 (or shortly thereafter).

Project background

The goal of this project is to leverage advanced machine learning techniques to improve personalized healthcare for individuals with chronic diseases through long-term sensing.

In this project, we are working with multimodal and heterogeneous data from diagnosis, physiotherapy, clinical interventions, physiological measurements, body metrics, robotics rehabilitation, among many others. We are developing advanced methods for feature extraction, data integration, and clustering from this growing body of clinical data. We will extract and analyze a large amount of information from longitudinal multi-modal clinical data (clinical systems and embodied sensing studies), to model the conditions and risk factors for making predictions about disease phenotypes, and disease progression.

As a team member, you will experience a range of exciting challenges, including multimodal data analysis through graphical models, developing innovative technologies for clinical decision support, monitoring health status and developing standardization methods for digital twins in different chronic conditions.

You will be based at ETH Zurich and collaborate with the team at the Swiss Paraplegic Center (SPZ) in Nottwil.

Job description

As the team lead for this exciting project, you will have the opportunity to work with a group of researchers to investigate the most efficient methods for sensor data fusion, training new algorithms for improving state-of-the-art graphical modelling from highly dynamic data, you will be responsible for the sensing-based identification of disease progression using multi-modal longitudinal data analysis, and dimensionality reduction for the creation of sparse models for disease progression.

More concretely, as PostDoctoral researcher, you will be responsible for the development of a method of digital twining for patients in chronic conditions for studying digital socio-psychological-biomarkers applicable in rehabilitation and daily life monitoring in SCI and elderly populations. With the goal of subsequent implementation of preventive treatment in different comorbidities. e.g., pressure injuries, infections, cardiovascular disease and sleep disorders.

If you are a highly motivated and creative individual with a passion for innovation, we want to hear from you.

Your profile

You have outstanding experience in mechatronics systems, and control with a PhD degree from a university in Computer Science , or related fields, with a proven track record in machine learning (Statistical machine learning, deep learning, causal graphical modelling).

  • Highly motivated, self-driven, and shows excellent performance
  • Strong analytical, mathematical, and algorithmic capabilities
  • Proven record of leading interdisciplinary projects
  • Adaptable and flexible to the continuous changes associated with research demands.
  • Proven track record in big data processing and deploying machine learning models into production (preferred)
  • Experience in digital health, mobile health, and biomedical sensor data acquisition (preferred)
  • Through your prior experiences, you have shown your understanding of modelling/analytics and a strong interest in healthcare.
  • Is closely familiar with bio-signals / biomarkers - either through studies or work experience and has a passion for healthcare support
  • Confirmed records on some of the methodological areas of interest: multi-sensing fusion, data analysis, time series modelling, graphical modelling, or deep learning
  • Proficiency in programming, preferably in Python and Java, SQL is a must

We offer

You will join a team of clinical and research scientists in the task of improving healthcare systems through physiological and clinical data systems design and analysis. The focus of this work will bring you close to intelligent health management while exploring various health data frameworks. You will experience multimodal data from robotic rehabilitation, digital data collection, general clinical practice, and detailed clinical studies focusing on interoperability, data transfer, and standardization among multiple clinical systems and devices.

We offer a full-time research position (100%) funded for 2 years and extendable up to 4 years with a competitive salary in accordance with ETH standards .

Presence is estimated at 20% time at ETH Zurich, and 80% at SPZ Nottwil, LU, with flexibility for some remote work possible.

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 in a single PDF with the following documents:

  • Statement of interests and profile match (1 page Max),
  • CV (2 pages max),
  • One of your publications on a related topic (linked to open-source code),
  • Names and contact information of 2 references,
  • Applications will be processed regularly until the position is filled.

    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 HEST can be found on our website . Questions regarding the position should be submitted to Dr. Diego Paez, Email (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.
    Research Field
    Computer science
    Years of Research Experience
    4 - 10

    Research Field
    Years of Research Experience
    4 - 10

    Research Field
    Medical sciences
    Years of Research Experience
    4 - 10

    Research Field
    Computer science
    Years of Research Experience
    4 - 10

    Research Field
    Years of Research Experience
    4 - 10

    Additional Information
    Website for additional job details

    Work Location(s)
    Number of offers available
    ETH Zürich
    Postal Code
    Rämistrasse 101

    Where to apply


    Postal Code


    View or Apply

    Similar Positions