Postdoc Wearable Data Analysis

Complexity Science Hub Vienna Postdoc Wearable Data AnalysisComplexity Science Hub Vienna

Wien, Austria

4 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

2 years minimum

Working hours

Regular working hours

Languages

English

Job source

devjobs.at

Tech stack

Data Analysis Bioinformatics Computer Programming EHealth Python (Programming Language) Machine Learning Information Technology

Job description

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., * 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.

Requirements

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., * The ability to work independently, contribute methodological and scientific ideas, and collaborate effectively within an interdisciplinary research team.
Proficiency in English., * A strong quantitative background and experience with statistical analysis, machine learning, computational modelling, or time-series analysis.
Programming experience in Python, R, or a comparable language and an interest in health data, digital health, complex systems, or network medicine.
Experience with wearable data, physiological signals, clinical research, ME/CFS, or post-COVID condition is advantageous but not required.