K.G. Jebsen Center for Genetic Epidemiology

K.G. Jebsen Center for Genetic Epidemiology

Now hiring

  • The Nidelven (river) in Trondheim

Short about us

The overall aim of the K.G. Jebsen Center for Genetic Epidemiology is to better understand human health and disease by studying genomic variation in populations. The center primarily works on the translational axis between population-based and laboratory-based research.

The center formalize collaborations between four strong local research groups and a world leading team of international collaborators at the interface of medicine, epidemiology, genetics, applied statistics, bioinformatics and system biology. 

Young scientist of the month

Young scientist of the month

Martina Hall

With a background in statistics, Martina is now doing a PhD in biotechnology, combining statistical modeling and network analysis with applications for biological data. Most of her projects involve analysis of large genetic datasets for different conditions, applying methods such as penalized regression and dimension reduction, differential co-expression networks, bipartite networks and Markov models.

Previous scientists

Previous "Young scientists"

Previous scientists

Ben´s research involves understanding the genetics of complex traits and diseases and the use of genetic data within epidemiological frameworks to make causal inference. His methodologies are Genetic Epidemiology and Mendelian Randomization.

Brooke is currently pursuing a PhD in Bioinformatics and Master of Arts in Statistics at the University of Michigan. She studies the genetics of cardiovascular and metabolic traits in large genetic datasets using statistical methods and high-throughput computing.

Mari is a Postdoctoral Fellow at the K.G Jebsen Center and a Clinical Resident at the Department of Dermatology, St. Olavs Hospital. Her work includes genetic studies using data from HUNT in combination with regional- and national health registries, and RNA sequencing data from psoriasis-related skin samples. She aims to reveal novel mechanisms for inflammatory skin diseases and to translate these insights into prevention strategies and treatments.

With a background in statistics, Martina is now doing a PhD in biotechnology, combining statistical modeling and network analysis with applications for biological data. Most of her projects involve analysis of large genetic datasets for different conditions, applying methods such as penalized regression and dimension reduction, differential co-expression networks, bipartite networks and Markov models.