Postdoctoral fellowship in wearable data science, multi-omics integration, and machine learning for precision cardiovascular health

We are looking to hire a highly motivated and dynamic Postdoc to join the Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark. Candidates should have a strong background in biostatistics, bioinformatics, data science, and/or machine learning, experience with high-resolution time-series data such as continuous glucose monitoring (CGM) or other wearable sensor data, and an interest in diabetes and cardiovascular disease. The position is a 2-year position to start March 1, 2027, or after agreement.

Background

The Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR) is an academic research Center that pioneers groundbreaking research towards better cardiometabolic health. Through collaborative interdisciplinary research from single-cell genomics to whole-body systems, CBMR aims to transform the basic understanding of cardiometabolic health and accelerate its translation into prevention and treatment strategies.

The Center’s uniquely multi- and interdisciplinary approach combines research in genetics, physiology and pharmacology, to better understand the complex interplay of the many factors that drive cardiometabolic disease. You can learn more in the Executive Summary of CBMR's Strategy 2024–2028 .

CBMR was established in 2010 at the Faculty of Health & Medical Sciences and has been located in the Maersk Tower at PANUM since 2017. The around 260 employees create an international, highly collaborative research environment across disciplines.

The position is available in the Merino Group and will be carried out in collaboration with SMARTbiomed. The Merino Group investigates how molecular and environmental factors modulate variable metabolic responses to therapeutic interventions with special emphasis on precision diabetes prevention.

SMARTbiomed is an international collaboration between Aarhus University, University of Oxford, and University of Copenhagen that aims to advance the development of methods and software for analysis and inference from massive human data, advancing applications in medicine. SMARTbiomed is anchored at the National Centre for Register-based Research (NCRR), Department of Public Health at Aarhus University, and led by Prof. Naomi Wray.

Job description

The successful candidate will join a project that integrates wearable and omics data to advance cardiometabolic health. Wearable and digital health devices now make it possible to monitor health and metabolic status continuously in free-living settings, from healthy individuals to advanced patient populations. Continuous glucose monitoring (CGM), for example, records interstitial glucose every 3-15 minutes for up to 14 days. Yet how best to analyze and use these high-resolution data remains an open question, and publicly available tools to do so are scarce.

The Postdoc will develop new statistical and machine-learning methods to handle and analyze high-resolution CGM data, moving beyond standard summary metrics to identify the distinct axes by which glucose becomes elevated.

These approaches, including interpretable deep-learning models for long glucose sequences, will be used to uncover disease subtypes concealed within the current broad definition of type 2 diabetes and to relate them to cardiometabolic risk, disease progression, response to interventions, and complications.

The candidate will further integrate CGM with other digital health data, such as physical activity, sleep, diet, and heart rate, and with genomics and other omics layers, to understand inter-individual susceptibility to dysglycaemia and to discover novel disease markers and pathways. A key output will be open-source software that makes these methods available to the research and clinical communities.

Skills and qualifications

We are looking for highly motivated and enthusiastic Postdoc who, in addition to the desired expertise stated above, have the following skills and qualifications:

• PhD in biostatistics, bioinformatics, computer science, data science, epidemiology, or a related quantitative field.
• Expertise in analyzing high-resolution time-series or longitudinal data, ideally from wearable devices such as continuous glucose monitors, accelerometers, or heart-rate sensors.
• Strong machine-learning skills, including deep learning for sequential data (e.g., transformer-based models), and experience working with large datasets. Experience integrating genomic or other omics data is a plus.
• High level of proficiency in programming (e.g., Python and/or R) and experience developing reproducible, open-source software.
• An active interest in diabetes, cardiovascular disease, and metabolism.
• A proven track record showing scientific productivity in peer-reviewed journals.
• Proficient communication skills and ability to work in multi-disciplinary teams.
• Experience in supervising other researchers at different levels.
• Excellent English communication skills, both written and spoken.

Place of employment

The place of employment is at the Novo Nordisk Foundation Center for Basic Metabolic Research, Maersk Tower, Panum Institute, University of Copenhagen. We offer a creative and stimulating work environment in a dynamic, international research setting.

Terms of employment

The employment as Postdoc is a full-time and fixed-term position for 2 years. The starting date is March 1, 2027, or after agreement.

Salary, pension and terms of employment will be in accordance with the agreement between the Ministry of Finance and AC (Danish Confederation of Professional Associations). Depending on qualifications, a supplement may be negotiated.

Non-Danish and Danish applicants may be eligible for tax reductions, if they hold a PhD degree and have not lived in Denmark in the last 10 years.

The position is covered by the Job Structure for Academic Staff at Universities 2025.

Questions

For further information, please contact Associate Professor Jordi Merino at [email protected] . For questions regarding the recruitment procedure, please contact SUND HR CBMR at [email protected] .

International applicants may find these links useful www.ism.ku.dk (International Staff Mobility) and https://www.workindenmark.dk/ .

Application procedure

Your online application must be submitted in English by clicking ‘Apply now’ below. Furthermore, your application must include the following documents/attachments – all in PDF format:

• Motivated letter of application (max. one page)
• CV incl. education, work/research experience, language skills and other skills relevant to the position
• A certified/signed copy of a) PhD certificate and b) Master of Science certificate.
• List of publications

Application deadline: November 29 , 2026, 23.59pm CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then immediately notified whether their application has been passed for assessment by an unbiased assessor. Once the assessment work has been completed each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.

You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/

The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.

When you will be affiliated with the University of Copenhagen or otherwise have access to their research, you may be required to undergo a background screening. The purpose is to protect the security and research integrity of the University of Copenhagen.

The University of Copenhagen wishes to reflect the diversity of society and welcome applications from all qualified candidates regardless of personal background.