Ph.D. Position: Personalized Prediction and Prevention of Dropouts in Digital Biomarker Studies
Techniques to self-manage motivation and behavior (Nature Human Behavior 2020); Detecting States of Receptivity in the Natural Environment (ACM IMWUT 2021); Wearable sensors enable personalized predictions (Nature Medicine 2021); Large language model AI chatbots require approval as medical devices (Nature Medicine 2023).
Exciting technological advances in wearables and biosensors rapidly transform how we monitor and manage metabolic health beyond finger pricking and blood picks. Instead, passive, wrist-worn data streams hold massive potential to provide personalized insights into glucose levels and to help inform decisions about diet, exercise, and medication management in everyday life and at scale.
The CSS Health Lab is a research laboratory at the Centre for Digital Health Interventions, a joint initiative of ETH Zurich and the University of St. Gallen (HSG), dedicated to various aspects of digital health and supported by CSS, one of the largest Swiss health insurance companies. Given the increasing health and economic burden of non-communicable diseases, the lab aims to make prevention measurable, actionable, and accountable and to make preventative care successful.
To strengthen the CSS Health Lab, we offer the following position at HSG’s School of Medicine in St. Gallen and ETH Zurich under the supervision of Mia Jovanova, PhD, upcoming Scientific Director of the CSS Health Lab and Postdoctoral Research in Digital Biomarkers for Healthy Longevity, with Prof. Dr. Tobias Kowatsch and Prof. Dr. Florian von Wangenheim being co-supervisors: Research Assistant to obtain a Ph.D. at ETH Zurich.
You must be eligible for a Ph.D. at ETH Zurich, and you will research the design, development, and evaluation of digital biomarkers in metabolic health with a specific focus on personalized prediction and prevention of dropouts in digital biomarker studies. As part of our team, you take direct project responsibility. You will design protocols for digital biomarker studies and identify and systematically assess intervention components that promote adherence to digital biomarker studies (e.g., based on behavioral economics, health psychology, marketing, and personalized motivational messages delivered by large language model AI chatbots). You will also develop methods to predict and prevent dropouts in such studies. You will work in a highly interdisciplinary team at the intersection of computer science, medicine, and business innovation.
Employment conditions, compensation, and benefits are attractive and based on the guidelines of ETH Zurich. The average duration for obtaining a Ph.D. is 3.5-4 years.
You should meet the following requirements:
- A master’s degree in behavioral sciences, with a GPA (Grade Point Average) of at least 5.0 (GPA of 2.0 and better in Germany and Austria) in combination with a strong interest in digital biomarkers
- Strong expertise in machine learning methods, especially with large language models.
- Strong background in experimental research design in digital health and health behavior
- Proficiency in programming languages and familiarity with data visualization techniques and tools for presenting complex data.
- Strong interest in metabolic health, healthy longevity, health economics, and technology-based innovation
- Prior experience in applied research projects, start-ups, or venture capital, as well as prior work experience in the health industry, is advantageous.
- Self-confident appearance and high conceptual and communication skills, especially regarding presenting research results to a broad and interdisciplinary audience
- Profound knowledge (written/oral) in German and English
If you are fascinated by the described task and would like to be part of a highly motivated, young team, we would be pleased to receive your electronic application via the following link: APPLY HERE
For all inquiries, please email Prof. Dr. Tobias Kowatsch and Giuliana Breu.