Integrative Omics & Biomedical Informatics Laboratory

Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA

© 2016 Dokyoon Kim, All Rights Reserved



We are seeking highly motivated individuals with a background in Biomedical Informatics, Computational Biology, Human Genetics, Statistical Genetics, Computer Science, and/or related quantitative fields for postdoctoral fellow positions at the Department of Biostatistics, Epidemiology and Informatics within the Perelman School of Medicine at the University of Pennsylvania.

Our research entails the development and application of data integration approach to improve the ability to diagnose, treat, and prevent complex diseases. Our primary focus lies in integrating multi-omics data, imaging and clinical/phenotype data derived from electronic health records (EHR) to better translate findings  into clinical products. We are currently accessing the data from the Penn Medicine Biobank (PMBB), which is one of the largest academic biobanks in the US, numbering > 60,000 participants and still actively recruiting. Participants consent to storage of biospecimens and their use in data generation, access to electronic health record (EHR) data, and permission for recontact. The PMBB has generated extensive genomic data, including ~12,000 whole exomes and ~20,000 genome‐wide chip genotyping to date, and facilitates the access to linked EHR phenotype data and stored plasma and serum on all participants. Importantly, all participants enrolled in the PMBB, both currently and prospectively, will have whole exome sequencing and chip genotyping performed.

Research in our group is highly collaborative, with broad research opportunities for exploring the genetic architecture of complex traits in diseases such as cancer, neurodevelopmental/psychiatric disorders, cardiovascular disease, pharmacogenomic traits, as well as rare and Mendelian diseases among others. Our projects have been both theoretical and applied, and they include developing novel data integration methods that combine multi-omics data and biological knowledge, predicting clinical outcomes based on interactions between multi-omic features, integrating multi-modal neuroimaging and multi-omics data, and identifying gene-gene (GxG) and gene-by-environment (GxE) interactions in several phenotypes/diseases. We plan to continue our work in these areas, focusing primarily on providing actionable clinical products based on inter-plays within/between different dimensional genomic data. In particular, our long-term research goal is to develop and evaluate sophisticated data integration methods that simultaneously combine peoples’ individual variations in genomic (‘omic) data, imaging data, phenotype data derived from EHR, and environment/lifelog data for advancing precision medicine.



The ideal candidate will have a desire to work within a team and will have strong scientific written, verbal, and electronic communication skills, such as manuscript writing and scientific presentation skills.

Additionally, candidates must have:

  • Experience manipulating large-scale genomic data and/or phenotype data derived from EHR and efficient utilization of computer clusters.

  • Familiarity with bioinformatics tools and databases for the analysis of genetics data.

  • Record of peer-reviewed publications.

  • Statistical analysis experience using R required and programming abilities such as python or perl is desired.



Ph.D. degree in the area of Biomedical Informatics, Computational Biology, Human Genetics, Statistical Genetics, Computer Science, and/or related quantitative fields or M.D with appropriate research experience required.

Interested and qualified candidates should send a CV, brief research statement, and three letters of references to


Potential graduate students should apply to the University of Pennsylvania's doctoral program in Genomics and Computational Biology (GCB) or Graduate Group in Epidemiology & Biostatistics (GGEB).


Rotating students from GCB or GGEB are welcome to join the Kim lab to explore the cutting-edge of biomedical and translational informatics research.

Interested candidates should send a CV and brief research statement to