Postdoc in Computational Genomics
Job Description
The University of Copenhagen is seeking a highly motivated and talented Postdoc fellow to commence on November 15, 2026, or after agreement, in the Kilpeläinen Group at the Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR). The Postdoc fellowship is part of the CBMR International PhD & Postdoc Program.
About us
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. CBMR was established in 2010 at the Faculty of Health & Medical Sciences and has been located in the Maersk Tower at Panum since 2017, with around 260 employees.
Our research
The Kilpeläinen group investigates the genetic and biological mechanisms through which genetic variation influences cardiometabolic disease, with a particular focus on mechanisms operating in adipose tissue. Using large-scale human genetic and population datasets, the group aims to move beyond association discovery to identify the causal variants, genes, and molecular pathways underlying cardiometabolic risk. Research integrates genome-wide association studies with multi-layer functional genomics — transcriptomic, epigenomic, and proteomic data — to generate biologically informed hypotheses, linked to functional readouts in relevant cellular models.
Job description
We are seeking a motivated researcher with strong computational expertise to join an interdisciplinary team working at the interface of statistical genetics, machine learning, and cardiometabolic research. The position focuses on developing and applying scalable and rigorous approaches for genetic discovery of cardiometabolic outcomes using biobank-scale data. Tasks involve evaluating and applying computational and statistical methods for genetic association studies under complex data conditions, including extensive phenotype missingness and heterogeneity across biological or environmental contexts such as age, sex, adiposity, or inflammatory state. You will work with large-scale population and biobank datasets from multiple international cohorts, comparing and integrating modern imputation, prediction, and surrogate-based inference approaches to support uncertainty-aware genetic discovery. Depending on background and interests, you may contribute to methodological development, large-scale data analysis, simulation and benchmarking studies, and cross-cohort validation, including integration of additional molecular data layers such as proteomics.
Required qualifications
- PhD (or near completion) in bioinformatics, statistical genetics, computational biology, biostatistics, data science, epidemiology, or a related quantitative or life-science discipline.
- Strong scientific track record relative to career stage, demonstrated by publications, preprints, or substantial contributions to large collaborative projects.
- Solid background in statistical modeling, machine learning, or applied data analysis, with interest in genetic association studies and complex trait genetics.
- Knowledge of genome-wide association studies (GWAS), polygenic scores, imputation methods, or related genetic epidemiology concepts.
- Experience working with large-scale datasets (e.g. biobank, omics, or electronic health record data) using programming languages such as Python, R, or similar.
- Familiarity with high-performance or cloud computing environments and reproducible research practices.
- Ability to work collaboratively in an interdisciplinary and international research environment, with excellent written and oral English.
Eligibility
Postdoc fellowships within the CBMR International PhD & Postdoc Program are open to applicants with a PhD degree awarded from a university outside Denmark. The program is also open to applicants with a Danish PhD degree who can document at least 12 months of full-time research experience from outside Denmark during or after their PhD studies. The PhD degree must be obtained before November 15, 2026. Applicants who have been employed in a postdoctoral position for more than one year at the University of Copenhagen prior to the commencement of the fellowship are not eligible.
The University of Copenhagen is seeking a highly motivated and talented Postdoc fellow to commence on November 15, 2026, or after agreement, in the Kilpeläinen Group at the Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR). The Postdoc fellowship is part of the CBMR International PhD & Postdoc Program.
About us
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. CBMR was established in 2010 at the Faculty of Health & Medical Sciences and has been located in the Maersk Tower at Panum since 2017, with around 260 employees.
Our research
The Kilpeläinen group investigates the genetic and biological mechanisms through which genetic variation influences cardiometabolic disease, with a particular focus on mechanisms operating in adipose tissue. Using large-scale human genetic and population datasets, the group aims to move beyond association discovery to identify the causal variants, genes, and molecular pathways underlying cardiometabolic risk. Research integrates genome-wide association studies with multi-layer functional genomics — transcriptomic, epigenomic, and proteomic data — to generate biologically informed hypotheses, linked to functional readouts in relevant cellular models.
Job description
We are seeking a motivated researcher with strong computational expertise to join an interdisciplinary team working at the interface of statistical genetics, machine learning, and cardiometabolic research. The position focuses on developing and applying scalable and rigorous approaches for genetic discovery of cardiometabolic outcomes using biobank-scale data. Tasks involve evaluating and applying computational and statistical methods for genetic association studies under complex data conditions, including extensive phenotype missingness and heterogeneity across biological or environmental contexts such as age, sex, adiposity, or inflammatory state. You will work with large-scale population and biobank datasets from multiple international cohorts, comparing and integrating modern imputation, prediction, and surrogate-based inference approaches to support uncertainty-aware genetic discovery. Depending on background and interests, you may contribute to methodological development, large-scale data analysis, simulation and benchmarking studies, and cross-cohort validation, including integration of additional molecular data layers such as proteomics.
Required qualifications
- PhD (or near completion) in bioinformatics, statistical genetics, computational biology, biostatistics, data science, epidemiology, or a related quantitative or life-science discipline.
- Strong scientific track record relative to career stage, demonstrated by publications, preprints, or substantial contributions to large collaborative projects.
- Solid background in statistical modeling, machine learning, or applied data analysis, with interest in genetic association studies and complex trait genetics.
- Knowledge of genome-wide association studies (GWAS), polygenic scores, imputation methods, or related genetic epidemiology concepts.
- Experience working with large-scale datasets (e.g. biobank, omics, or electronic health record data) using programming languages such as Python, R, or similar.
- Familiarity with high-performance or cloud computing environments and reproducible research practices.
- Ability to work collaboratively in an interdisciplinary and international research environment, with excellent written and oral English.
Eligibility
Postdoc fellowships within the CBMR International PhD & Postdoc Program are open to applicants with a PhD degree awarded from a university outside Denmark. The program is also open to applicants with a Danish PhD degree who can document at least 12 months of full-time research experience from outside Denmark during or after their PhD studies. The PhD degree must be obtained before November 15, 2026. Applicants who have been employed in a postdoctoral position for more than one year at the University of Copenhagen prior to the commencement of the fellowship are not eligible.
Terms of employment
Full-time position for 2 years, starting November 15, 2026, or after agreement. Salary, pension and terms of employment follow the agreement between the Danish 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 for the last 10 years.
Application deadline: August 10, 2026, 23:59 CET. Applications must be submitted in English via the 'Apply for position' link on the posting.
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