PhD Position: Digital Biomarkers for Type-2 Diabetes Management at School of Medicine
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PhD Position: Digital Biomarkers for Type-2 Diabetes Management at School of Medicine (m/w/d), St. GallenClient:University of St.Gallen
Location:St. Gallen
Job Category:Education
Job Reference:f0198a5747a1
Job Views:5
Posted:14.03.2025
Expiry Date:28.04.2025
Job Description:Your tasks
To strengthen the CSS Health Lab, we offer the following position at Med-HSG in St. Gallen under the supervision of Mia Jovanova, PhD, Scientific Director of the CSS Health Lab and Postdoctoral Research in Digital Biomarkers for Healthy Longevity at the School of Medicine, University of St. Gallen, 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 and algorithm development and validation using time series data.
You must have a strong technical background in pre-processing 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.
Your profileYou should meet the following requirements:
- Strong expertise in pre-processing and modeling time series data from wearables using machine learning.
- Strong expertise in Python, R, or similar software/languages and ability to independently run pre-processing and machine learning pipelines for wearable data.
- 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) in combination with strong experience in wearables and biosensor technology.
- Strong interest in metabolic health, healthy longevity, health economics, and technology-based innovation.
- Interest in applied research projects, start-ups, or venture capital, as well as prior work experience in the digital 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 English and German (advantageous).
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