PhD in Digital Health: Post-Sepsis Outcomes & ML

University of Zurich; Zürich; On-site; CHF 56,000 - 67,000; Full time

Job summary

University of Zurich in Switzerland invites applications for a PhD position embedded in the UMZH Learn4Sepsis project. The role focuses on designing a digital follow-up cohort post-sepsis, co-creating minimal data items and patient-reported outcomes, and developing an adaptive survey with NLP-enabled features.

ideal candidates hold a MSc in Statistics, ML, Biomedical Sciences, or related fields, with strong programming (R/Python) and a keen interest in digital health and stakeholder engagement.

Qualifications

• Requires a quantitative Master's degree in a relevant field.
• Strong interest in person-centered health research and digital health technologies.
• Excellent programming skills (R, Python) and data management experience.
• Experience with or willingness to develop NLP skills and machine learning methods.

Responsibilities

• Design and co-create a minimal data set and patient-reported outcomes for post-Sepsis.
• Co-develop conceptual and technical elements for the adaptive survey (including knowledge trees).
• Gather and analyze contextual and patient-reported outcome data; develop prediction algorithms using statistics and ML.
• (Optional) collect and analyze wearable sensor data.

Skills

R

Python

Machine learning

Time series analysis

Natural Language Processing

Education

Master's degree in Statistics or ML or Biomedical Sciences or related field

Tools

Knowledge graphs

Job description

University of Zurich in Switzerland invites applications for a PhD position embedded in the UMZH Learn4Sepsis project. The role focuses on designing a digital follow-up cohort post-sepsis, co-creating minimal data items and patient-reported outcomes, and developing an adaptive survey with NLP-enabled features.

ideal candidates hold a MSc in Statistics, ML, Biomedical Sciences, or related fields, with strong programming (R/Python) and a keen interest in digital health and stakeholder engagement.