Postdoc: Wearable Data Science for Precision Cardio

KU; København; On-site; DKK 480,000 - 560,000; Full time

Job summary

KU in Copenhagen invites applications for a two-year Postdoctoral fellowship in wearable data science, multi-omics integration and machine learning for precision cardiovascular health.

Based at the Novo Nordisk Foundation Center for Basic Metabolic Research, you will develop statistical and ML methods to analyze high-resolution CGM and omics data and to uncover disease subtypes relevant to diabetes and cardiometabolic risk.

Qualifications

• PhD in biostatistics, bioinformatics, computer science, data science, epidemiology, or a related quantitative field.
• Experience with high-resolution time-series or longitudinal data from wearable devices such as CGM, accelerometers, or heart-rate sensors.
• Strong machine-learning skills, including deep learning for sequential data, and experience with large datasets.
• Proficiency in programming with Python and/or R and developing reproducible, open-source software.
• Active interest in diabetes, cardiovascular disease, and metabolism.
• Proven track record of scientific productivity in peer‑reviewed journals.
• Excellent communication skills and ability to work in multi-disciplinary teams.
• Experience supervising other researchers at different levels.
• Excellent English communication skills.

Skills

Time-series analysis

Machine learning

Python / R

Wearable data

Multi-omics

Communication skills

Supervision

English fluency

Education

PhD in biostatistics / bioinformatics / data science / epidemiology

Job description

KU in Copenhagen invites applications for a two-year Postdoctoral fellowship in wearable data science, multi-omics integration and machine learning for precision cardiovascular health.

Based at the Novo Nordisk Foundation Center for Basic Metabolic Research, you will develop statistical and ML methods to analyze high-resolution CGM and omics data and to uncover disease subtypes relevant to diabetes and cardiometabolic risk.