Postdoc: Wearable Data Science for Metabolic Health

University of Copenhagen; Denmark; Presencial; DKK 420.000 - 540.000; Jornada completa

Descripción de la vacante

The Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen invites applications for a 2-year postdoctoral position focused on developing statistical and machine-learning methods for high-resolution CGM data. You will work within the Merino Group and SMARTbiomed, collaborating across genomics, omics, and digital health to advance cardiometabolic health.

Ideal candidates have a PhD in a quantitative field, strong ML experience with time-series data, and

Formación

• PhD in biostatistics, bioinformatics, computer science, data science, epidemiology, or a related quantitative field.
• Experience with high-resolution time-series data or wearable sensors.
• Strong machine-learning skills including deep learning for sequential data.
• Proficiency in Python and/or R.
• Excellent English communication skills, written and spoken.

Responsabilidades

• Develop new statistical and machine-learning methods to handle high-resolution CGM data.
• Integrate CGM with other digital health data and omics layers.
• Contribute to open-source software providing access to developed methods.
• Collaborate within multi-disciplinary teams and supervise junior researchers.

Conocimientos

Biostatistics

Bioinformatics

Machine learning

Python/R

English communication

Supervising researchers

Collaboration

Educación

PhD in a quantitative field

Herramientas

Python

R

CGM data analysis

Open-source software

Descripción del empleo

The Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen invites applications for a 2-year postdoctoral position focused on developing statistical and machine-learning methods for high-resolution CGM data. You will work within the Merino Group and SMARTbiomed, collaborating across genomics, omics, and digital health to advance cardiometabolic health.

Ideal candidates have a PhD in a quantitative field, strong ML experience with time-series data, and