Jordi Merino
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Postdoctoral fellowship in wearable data science, multi-omics integration, and machine learning for precision cardiovascular health University of Copenhagen in Denmark
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Nov 29, 2026
Country
Denmark
University
University of Copenhagen
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About this position
This postdoctoral fellowship is based at the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen, where the successful candidate will join an interdisciplinary and highly collaborative environment focused on cardiometabolic health.
The project sits at the intersection of wearable data science, multi-omics integration, and machine learning. It will use high-resolution continuous glucose monitoring and other digital health streams, together with genomics and additional omics layers, to study how glucose regulation varies between individuals and how these differences relate to cardiometabolic risk, disease progression, treatment response, and complications.
The postdoc will work on developing new statistical and machine-learning methods for long, high-frequency glucose sequences, including interpretable deep-learning approaches. A central aim is to move beyond standard summary metrics and identify distinct patterns of glucose elevation that may reveal subtypes within type 2 diabetes. The project also extends to integrating physical activity, sleep, diet, and heart-rate data with molecular profiles to uncover disease markers and pathways.
The position is located in the Merino Group and will be carried out in collaboration with SMARTbiomed, an international collaboration linking Aarhus University, the University of Oxford, and the University of Copenhagen. The wider environment at CBMR combines genetics, physiology, pharmacology, and single-cell genomics within an international research setting at the Maersk Tower, PANUM Institute.
A key outcome of the fellowship will be open-source software that can support both research and clinical communities working with these data types.
Funding details
Available
What's required
Applicants must hold a PhD in biostatistics, bioinformatics, computer science, data science, epidemiology, or a related quantitative field. The post requires expertise in high-resolution time-series or longitudinal data, ideally 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; programming proficiency in Python and/or R; experience developing reproducible open-source software; interest in diabetes, cardiovascular disease, and metabolism; a proven publication record; strong communication skills; ability to work in multidisciplinary teams; experience supervising researchers at different levels; and excellent written and spoken English.
How to apply
Submit the application online in English via the Apply now portal. Include a one-page motivation letter, CV, certified/signed PhD certificate and Master of Science certificate, and list of publications. Apply by the deadline and ensure all documents are in PDF format.
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Professors
Associate Professor at University of Copenhagen
Denmark
Research Interests
Statistics
Artificial Intelligence
Medical Science
Computer Science
Omics
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