Postdoctoral Position in Single-Cell Cancer Genomics
A postdoctoral scholar position in the area of Single Cell Cancer Genomics is available in the laboratory of Dr. Allegra Petti in The Division of Oncology at Washington University School of Medicine in St. Louis.
We use computational approaches to analyze, integrate, and interpret large-scale genomic data -- with an emphasis on single-cell RNA-sequencing data -- to better understand heterogeneity and gene regulatory networks in human cancer. We participate in long-term, data-rich collaborations with established labs at Wash. U., in which quantitative analysis is closely integrated with clinical studies and molecular biology. The postdoctoral scholar will be involved in projects that address topics such as intratumoral expression heterogeneity and tumor evolution in AML, and the interplay of genetic, epigenetic, and immunological heterogeneity in solid tumors. Opportunities to initiate and lead projects, as well as to participate in ongoing group projects, are plentiful.
The postdoc will work in a highly collaborative environment, involving extensive interaction with computational biologists, programmers, molecular biologists, clinicians, and other faculty in the Division of Oncology and the McDonnell Genome Institute.
Primary Duties and Responsibilities
- Implement and develop statistical methods and automated pipelines to analyze and visualize high throughput sequencing data, particularly single-cell and bulk gene expression data.
- Integrate genetic and epigenetic information about tumor samples, including mutation, gene expression, and methylation data.
- Perform and develop integrated pathway and gene network analyses related to cancer.
- Evaluate and design experimental plans for sequencing-based projects studying the genetic basis of cancer.
- Analyze and interpret results, read the literature, communicate with collaborators, and produce scientific publications.
A doctoral degree in computational biology, bioinformatics, computer science, statistics, applied math, biology, or a related field, with a solid background in programming and machine learning/statistics.
The successful candidate will be self-motivated, adept at critical thinking, collaborative, and eager to acquire new knowledge and skills on a regular basis. The ability to analyze and interpret results, communicate with others, and produce scientific publications is essential.
Please send a curriculum vitae, statement of research interests, and contact information for three references to Dr. Allegra Petti at email@example.com.