Postdoc Position: Wearable Data Analysis for ME/CFS and Post-COVID

Complexity Science Hub; Wien; Vor Ort; EUR 55.000 - 75.000; Vollzeit

Benefits dieser Stelle

Fully funded 2-year position

International team

Access to large-scale wearabledatasets

Professional development workshops

Training in ethical handling of real‑d

Zusammenfassung

Complexity Science Hub in Vienna invites applications for a two-year, fully funded postdoctoral researcher position within the TRACK-PEM project. You will develop and apply modern data science and machine learning methods to large-scale wearable health data to study PEM in ME/CFS and post-COVID condition.

The role emphasizes time-series analysis, methodological development, and collaboration across computational health, epidemiology, and clinical research, with opportunities for leadership and

Qualifikationen

• A completed doctoral degree or equivalent qualification in data science, statistics, mathematics, computer science, physics, biomedical engineering, bioinformatics, epidemiology, public health, or a related quantitative field.
• Strong quantitative background and experience with statistical analysis, machine learning, computational modelling, or time-series analysis.
• Ability to work independently, contribute methodological and scientific ideas, and collaborate effectively within an interdisciplinary research team.
• Programming experience in Python, R, or a comparable language and an interest in health data, digital health, complex systems, or network medicine.
• Proficiency in English.
• Experience with wearable data, physiological signals, clinical research, ME/CFS, or post-COVID condition is advantageous but not required.

Aufgaben

• Develop and apply modern data science and machine learning methods to large-scale longitudinal wearable and health data.
• Investigate whether patterns in wearable data can be used to identify and classify PEM and related health changes.
• Engage in methodological development and analysis of complex time-series data with attention to individual variability and real-world health data.
• Collaborate across researchers in computational health, network medicine, epidemiology, data science, and clinical research.
• Contribute to related research and develop collaborative methodological and scientific work within the wider group.

Kenntnisse

Quantitative background

Statistical analysis

Machine learning

Time-series analysis

Computational modelling

Python

R

Ausbildung

Doctoral degree in a quantitative field

Tools

Python

R

Jobbeschreibung

WE SEARCH FOR

We are looking for a postdoctoral researcher to join the TRACK-PEM project, which investigates post-exertional malaise (PEM) in people with ME/CFS and post-COVID condition.

PEM is a worsening of symptoms following physical or mental activity. At present, PEM is mainly assessed through interviews and questionnaires, while objective measures that capture how it develops in daily life are still lacking.

The successful candidate will develop and apply modern data science and machine learning methods to large-scale longitudinal wearable and health data. A central aim of the project is to investigate whether patterns in wearable data can be used to identify and classify PEM and related changes in health. The work will involve methodological development and analysis of complex time-series data, with a particular focus on individual variability and real-world health data.

The successful candidate will be embedded in a research group dedicated to understanding healthcare systems as complex adaptive systems and will work closely with researchers across computational health, network medicine, epidemiology, data science, and clinical research. They will have opportunities to contribute to related research beyond the immediate TRACK-PEM project and to develop collaborative methodological and scientific work within the wider research group.

YOUR PROFILE

• A completed doctoral degree or equivalent qualification in data science, statistics, mathematics, computer science, physics, biomedical engineering, bioinformatics, epidemiology, public health, or a related quantitative field
• A strong quantitative background and experience with statistical analysis, machine learning, computational modelling, or time-series analysis
• The ability to work independently, contribute methodological and scientific ideas, and collaborate effectively within an interdisciplinary research team
• Programming experience in Python, R, or a comparable language and an interest in health data, digital health, complex systems, or network medicine
• Proficiency in English
• Experience with wearable data, physiological signals, clinical research, ME/CFS, or post-COVID condition is advantageous but not required.

WE OFFER

• A fully funded 2-year postdoctoral position in an interdisciplinary and international research team
• The opportunity to work on an important and challenging health research project
• Access to clinical and large-scale wearable datasets
• Individualized guidance from an international team of advisors
• Scientific leadership and professional development workshops
• Practical experience aligned with career goals in academia, government, or industry
• Training in the ethical and technical aspects of working with real-world health data

CSH is committed to equal employment opportunity. Employment decisions are based on the requirements of the position, qualifications, merit, and organizational needs. We encourage applications from groups that are underrepresented in science. Personal data are processed in accordance with applicable law and the CSH data protection policy.