PhD in Computational Genomics and Precision Medicine
Develop scalable AI-driven methods for genomic data analysis to advance precision medicine in cardiometabolic health.
Best for:Best for early-career researchers with a background in bioinformatics, computational biology, or related fields, passionate about developing scalable AI methods for genomics and precision medicine.
Overview
This PhD position at the Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), University of Copenhagen, focuses on creating advanced computational methods for analyzing large-scale genomics data. The project aims to improve disease risk prediction and genetic understanding of cardiometabolic diseases by integrating genomic, multi-omics, and health data. The successful candidate will work in an interdisciplinary team combining machine learning, computational biology, and clinical research, contributing to AI-driven digital twins for precision health. The position offers a supportive, international environment with access to world-leading biomedical datasets and a focus on equitable and accurate genomic medicine.
What you'll do
- Develop scalable computational methods for analyzing biobank-scale genomics data to improve disease risk prediction.
- Integrate large-scale genomic, multi-omics, and health data to uncover patterns of genetic ancestry and disease-associated variation.
- Create and validate AI-driven models for equitable and accurate genomic precision medicine.
- Collaborate with interdisciplinary teams to advance AI-driven digital twins for cardiometabolic health.
- Publish research findings in high-impact journals and present results at international conferences.
- Contribute to open science initiatives and foster a collaborative research environment.
Requirements
Degree required: Master's degree in Bioinformatics, Precision Medicine, or a related scientific field (equivalent to a Danish two-year master's degree).
Degree offered: PhD
Language: English; proficiency required for academic communication and collaboration.
Instruction: English
- Proven academic excellence with a strong grade point average.
- Experience with biobank-scale datasets (e.g., All of Us, UK Biobank).
- Proficiency in developing and validating large-scale diagnostic analyses.
- Familiarity with integrating evolutionary information in computational models.
- Excellent written and oral English communication skills.
- Open to early-career researchers with a basic science background.
- No specific nationality or residency restrictions mentioned.
- No age limits or prior experience caps explicitly stated.
Funding & terms
How to apply
Deadline: 2026-10-01 (CET)
Submit an online application in English via the 'Apply now' link. Include a motivated letter of application, CV, certified copy of Master's diploma and transcript, list of publications, and contact details for at least two references. All documents must be in PDF format.
- Motivated letter of application (max. one page)
- CV including education, experience, language skills, and relevant skills
- Certified copy of original Master's diploma and transcript of records (with English translation if needed)
- List of publications
- References (name and contact details of at least two references)
Contact
Jonas Meisner / Simon Rasmussen· Assistant Professor / Professor
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