Computational Population Biology Group

Computational Population Biology group develops and applies methods for the integrative analysis of large-scale biological, epidemiological and clinical data. Our goals are to improve the understanding of how various omics affect the complex traits and to make use of such insights to improve the diagnosis, prevention and treatment of diseases whenever possible.

The group uses the latest approaches in genomics, medical imaging, computer science, statistics and machine learning to sort through increasingly rich and massive amount of data.

The group has affiliation with two departments: Epidemiology, Radiology and Nuclear Medicine.

The Radiology Department and specifically the Biomedical Imaging Group Rotterdam (BIGR), has years of expertise in machine learning algorithms and medical infrastructure development. Additionally, the group has a large GPU cluster, which makes it perfect place for our AI related projects.

The Epidemiology Department has a long history of successful epidemiological studies, unique facilities of the Rotterdam Study (ERGO study) and the Netherland Institute for Health Sciences (NIHES) educational program, which are important components for knowledge dissemination. Additionally, the Epidemiology department is well established within the CHARGE consortium.

Expertise Big data analysis - Omics - Bioinformatics - Epidemiology - Software development - Machine learning - Deep learning - Data Science - Data visualization - 3D Medical Imaging