Postdoctoral Fellowship - Computational and statistical approaches to inform and improve therapies for pediatric cancer

St. Jude Children's Research Hospital
Computational Biology
United States Tennessee Memphis


The Geeleher lab is seeking outstanding candidates for a postdoctoral fellowship focused on developing innovative computational and statistical approaches to inform and improve therapies for pediatric cancer and other diseases. Two projects are available. 1) Developing machine learning approaches for integration of pre-clinical, clinical genomics and electronic health record data for drug re-purposing and pharmacogenomics of anticancer agents. Identifying targeted therapies for patients with cancer is a central focus at St. Jude, and genome and RNA sequencing of patients’ tumors is now being performed regularly as part of standard-of-care. A major goal of the Geeleher lab is to explore machine learning approaches to prioritize targetable variants and expand the scope of targeted therapeutics. The postdoc will explore, optimize and build on emerging informatics techniques, including integrating somatic variation with transcriptomic variation and with protein-protein interaction networks. The Geeleher lab’s wet-lab component provides a platform for validation of computational predictions and discoveries. Successful completion of the project has the potential for a direct positive impact on patient care and high-impact publications. 2) Developing statistical methods for integrating single cell and bulk tissue expression data to understand the relationship between common inherited genetic variation, gene expression, and drug response. Chemotherapeutic response is a complex trait influenced by numerous factors. Inherited genetic variants influencing gene expression (expression quantitative trait loci, or eQTLs) have been identified as major contributors. However, our recent work has shown that the degree of influence of eQTLs on gene expression in cancer is less well understood than previously thought. The postdoc will explore how inherited genetic variation influences cancer risk, disease progression and drug response, building on methods developed in the lab to deconvolute eQTL signals from bulk tissue expression data to specific cell types. This work will aim to improve our understanding of inherited genetic variation in cancer and yield computational approaches applicable to a broad variety of complex traits and diseases.


PhD in a related field

Start date

As soon as possible

How to Apply

If interested, email your CV and contact information for three reference providers to Please mention this advertisement in your email.


Deanna Tremblay