Wearable and digital devices to monitor and treat metabolic diseases (Nature Metabolism, 2023), Harnessing wearables and mobile phones to improve glycemic outcomes with automated insulin delivery (The Lancet Digital Health, 2023), Noninvasive hypoglycemia detection in people with diabetes using smartwatch data (Diabetes Care, 2023), Digital twins and artificial intelligence in metabolic disease research (Trends in Endocrinology & Metabolism, 2024), Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes (npj Digital Medicine, 2024)

Technological advances in wearables and biosensors are rapidly transforming how we monitor and manage diabetes, moving beyond finger pricking and blood picks. Data from wearables and smartphones hold massive potential to provide personalized insights into blood glucose levels and to inform decisions about diet, exercise, in everyday life and at scale. What are the latest technological developments across wearables and biosensors, and how can we leverage them for digital biomarker development in diabetes management?

The CSS Health Lab is a research laboratory at the Centre for Digital Health Interventions, a joint initiative of the University of St. Gallen and ETH Zurich, 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 the School of Medicine, University of St. Gallen (MED-HSG) under the supervision of Mia Jovanova, PhD, Scientific Director of the CSS Health Lab, CDHI Core Director for Digital Biomarkers in Metabolic Health at MED-HSG, with Prof. Dr. Tobias Kowatsch and Prof. Dr. Elgar Fleisch being co-supervisors: Research Assistant to obtain a Ph.D. in Management, at HSG.

You must be eligible for a Ph.D. at the University of St. Gallen, and you will work on developing novel digital monitoring biomarkers for diabetes management using passive sensing wearable data (e.g., V02max, blood pressure, heart rate variability) and smartphones. As part of our team, you take direct project responsibility. You will lead machine learning pipelines for feature engineering, algorithm development, and validation using time series data.

You must have a strong technical background in preprocessing longitudinal, time series data from wearables and statistically modeling time series data using machine learning methods. You will work in a highly interdisciplinary team at the intersection of computer science, behavioral medicine, clinical psychology, and business innovation.

Employment conditions, compensation, and benefits are attractive and based on the guidelines of HSG. The average duration for obtaining a Ph.D. is 3.5-4 years. You should meet the following requirements:

1. Strong expertise in pre-processing and modeling time series data from wearables using machine learning.
2. Strong expertise in Python, R, or similar software/ languages and ability to independently run pre-processing and machine learning pipelines for wearable data.
3. A master’s degree in computer science or engineering, with a GPA (Grade Point Average) of at least 5.0 (GPA of 2.0 and better in Germany and Austria) and a strong experience in wearables and biosensor technology.
4. Strong interest in metabolic health, healthy longevity, health economics, and technology-based innovation.
5. Interest in applied research projects, start-ups, and venture capital, as well as prior work experience in the digital health industry, are advantageous.
6. Self-confident appearance and high conceptual and communication skills, especially regarding presenting research results to a broad and interdisciplinary audience.
7. Profound knowledge (written/oral) in English and German (advantageous).

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