Postdoc: Wearable Data Science for Precision Cardio Health

KU - SUND - PANUM KBH N; Region Hovedstaden; Presencial; DKK 420.000 - 520.000; Jornada completa

Descripción de la vacante

The successful candidate will join a project that integrates wearable and omics data to advance cardiometabolic health. Wearable devices like CGM enable continuous monitoring, and the postdoc will develop methods to analyze these high-resolution data beyond standard metrics.

The role includes integrating CGM with other digital health data and omics layers to understand inter-individual susceptibility and to help identify novel disease markers and pathways.

Formación

• PhD in a quantitative field with strong biostatistics or bioinformatics background.
• Experience with high-resolution time-series or wearable device data (e.g., CGM).
• Strong machine-learning skills, including deep learning for sequential data.
• Proficiency in Python and/or R and open-source software development.
• Interest in diabetes, cardiovascular disease, and metabolism.
• Proven scholarly productivity in peer-reviewed journals.
• Excellent communication and ability to work in multi-disciplinary teams.
• Experience supervising researchers.

Responsabilidades

• Develop new statistical and machine-learning methods for high-resolution CGM data.
• Integrate CGM with other digital health data and omics layers.
• Produce open-source software for the research community.
• Collaborate with Merino Group and SMARTbiomed.

Conocimientos

Wearable data analysis

High-resolution time-series analysis

Python and/or R

Machine learning (deep learning)

Interdisciplinary collaboration

Educación

PhD in biostatistics / bioinformatics / computer science / data science / epidemiology

Herramientas

Python

R

Descripción del empleo

The successful candidate will join a project that integrates wearable and omics data to advance cardiometabolic health. Wearable devices like CGM enable continuous monitoring, and the postdoc will develop methods to analyze these high-resolution data beyond standard metrics.

The role includes integrating CGM with other digital health data and omics layers to understand inter-individual susceptibility and to help identify novel disease markers and pathways.