ETH Zürich

Postdoctoral Researcher in Explainable Machine Learning for Health Status Monitoring and Modelling

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ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

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Postdoctoral Researcher in Explainable Machine Learning for Health Status Monitoring and Modelling

Are you a highly motivated and enthusiastic researcher 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 reducing co-morbidities through new healthcare systems using physiological and clinical information analysis for a closed-loop decision support system that can be verified in rehabilitation in many health conditions. 

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. Focused on developing forward patient status simulations with multimodal, multiorgan, and environmental information for personalized healthcare state estimation and disease tracking. Development of novel algorithms will be performed in chronic conditions of elderly, Stroke and SCI.
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 year and renewal up to 4 years. 
Ideal starting date: May 2024 (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

This postdoctoral position would focus on studying the properties of different Graphical Modellings (such as GANS, VAEs, and Diffusion Models) within the context of health information fusion for disease onset prediction that build upon our laboratory's past and ongoing work. As the team lead for this exciting project, you will have the opportunity to work with a group of sensing, clinical, and social researchers to investigate graphical modelling for healthcare decision support.

You will be responsible for the multi-modal longitudinal data analysis and the creation of sparse models that can be mapped to known standards of functioning ability. More concretely, as Post-doctoral researcher, you will be responsible for researching methods 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.
Moreover, the successful candidate will have the opportunity to support the development of new machine learning lectures in the field of healthcare.

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 Machine Learning with a PhD degree from a university in Computer Science, or related fields, with a proven track record in statistical machine learning, deep learning, and graphical modelling.

  • Highly motivated, self-driven, and shows excellent performance.
  • Strong analytical, mathematical, and algorithmic capabilities.
  • Proficiency in programming, preferably in Python.
  • Proven record of leading interdisciplinary projects (desirable).
  • Adaptable and flexible to the continuous changes associated with research demands.
  • Through your prior experiences, you have shown your understanding of modelling/analytics and a strong interest in healthcare.
  • Confirmed records on some of the methodological areas: graphical neural networks, time series modelling, graphical modelling.
  • Proven track record in deploying machine learning models into production (preferred)
  • Passion for healthcare and medical decision support.

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 funded 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. Flexibility to travel is expected for 1-2 times per year to our partners in Japan.

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 self-assessment of your profile match (1 page Max) with the subject line: "AI in healthcare is transparent or null",
  • CV (2 pages max),
  • One of your publications on a related topic (linked to open-source code),
  • Names and contact information of 2 references,

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Applications will be revised until the position is filled.

Further information about HEST can be found on our website Questions regarding the position should be submitted to Mrs. Ai Sullivan, 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.

Job details

Postdoctoral Researcher in Explainable Machine Learning for Health Status Monitoring and Modelling
Rämistrasse 101 Zurich, Switzerland
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About the employer

ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

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