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Your responsibilities
This PhD project is embedded in Work Package 5 of the UMZH Learn4Sepsis project and aims to design and initi-ate a digital follow-up cohort of persons after sepsis. For this, we will co-create a set of minimal data items and patient-reported outcomes to describe post-sepsis on different health- and daily life domains. By refining existing tools, we will develop and release an adaptive survey (including voice input options) to pilot a scalable, longitu-dinal study into life after sepsis.
The tasks of this PhD position are:
* Co-designing a minimal dataset and patient-reported outcomes for post-Sepsis
* Co-developing conceptual and technical elements for setting up the adaptive survey (Key elements: knowledge trees/ontologies, database systems, natural language processing)
* Gathering and analyzing contextual and patient-reported outcome data (e.g. using - Developing and validating prediction algorithms using classical statistics and machine learning tools
* (optional) Collection and analysis of wearable sensor data
Your profile
We are seeking a highly motivated candidate with:
* A quantitative Master's degree (MSc or equivalent) in Statistics, Machine learning, Biomedical Sciences, Bioengi-neering, or a related field
* Strong interest in person-centered health research and digital health technologies
* Willingness to interact with stakeholders (patients, clinicians)
* Some experience in information- and data management (experience with database systems, knowledge graphs are an asset)
* Excellent programming or data science skills (R, Python), e.g. for time series analysis, machine learning methods for prediction
* Experience in or willingness to acquire skills in Natural Language Processing
* Excellent command of written and spoken English
* Good communication skills in English (German is an asset) and capacity for interdisciplinary collaboration
Applicants must fulfil eligibility criteria for Swiss-based PhD positions.
Marco Kaufmann
PhD program coordinator
epibiostat-phd@ebpi.uzh.ch