Specialty: Computational Biology
Program: Cancer Genome Analysis
Job Description: The Broad Institute of MIT & Harvard is looking to hire an exceptional candidate to join the team leading data production and analyses for the Genotype-Tissue Expression Consortium (commonfund.nih.gov/GTEx and gtexportal.org). The goal of this initiative is to increase our understanding of gene regulation across tissues in the human body, and how changes in gene expression and splicing contribute to common human diseases, with the ultimate goal of improving health care. By investigating genetic effects on gene regulation, the project aims to identify the specificity and variation of genetic regulation across tissues and ultimately across cell types. The successful candidate will join an interdisciplinary team working with an unprecedented set of RNA sequence data from a wide range of human tissues and whole genome and exome sequence data from the tissue donors. The scope of this project provides unique opportunities for developing and learning novel analytical methods for RNA and DNA sequence data, including transcript expression quantification, mapping of quantitative trait loci, multi-tissue analyses, and integration with epigenetic and GWAS data sets.
We are looking for an individual who is enthusiastic about taking a hands-on approach to complex problems within a collaborative team of computational biologists, software engineers, and experimental scientists in a collegial environment infused with intellectual rigor. The successful candidate will demonstrate innovative and analytical thinking, will have outstanding academic records, an excellent technical background, strong communication skills, and will enjoy working in an interdisciplinary team. Familiarity with genomic data analysis is preferred but not required; foremost, we are looking for a candidate with a strong desire to contribute to this field. Working in our group will introduce the candidate to cutting-edge genomic and computational technologies (e.g., cloud-based approaches recently developed in the group as part of the NCI Cancer Genomics Cloud Pilot initiative).
The Broad Institute provides a vibrant research environment with close ties to MIT, Harvard and Harvard-affiliated hospitals across Boston. Moreover, by becoming a member of our initiative, the candidate will have the opportunity to see his/her contributions utilized and recognized by the GTEx Consortium, which constitutes a vast, global network of researchers in the fields of genomics, biostatistics and computational biology.
- Expand existing data production pipelines by integrating methods developed in the consortium and larger research community and by developing novel approaches. This process involves developing software prototypes, improving them to production quality, and incorporating them into existing production infrastructure (e.g., for cloud-based environments)
- Develop data analysis strategies, write algorithms, and deploy computational tools for the analysis of large genomic data sets. Critically review, analyze, and communicate results to biologists, computational biologists, and software engineers, and contribute to publications.
- Develop, run and interpret quality control procedures for genotype and gene expression data.
- Develop summary statistics, figures, and reports for analytical pipelines, to automate the unbiased evaluation of data quality.
- Contribute statistical and computational analyses to research projects centered on genetic regulation of gene and isoform expression across multiple tissues.
- Work closely with a multidisciplinary team to explore large genomic data sets and interpret observations.
- B.S. in computer science, mathematics, (bio)statistics, bioinformatics, or a related quantitative discipline required. M.S. in these fields preferred.
- Strong programming skills and in-depth experience with multiple programming languages required, preferably C++ and/or Java, and at least one among Python, R, and Matlab.
- Experience with computational analysis, algorithm development and statistics is required. Experience analyzing biological data in a research setting preferred.
- Experience with Unix/Linux environments required (including shell scripting).
- Ability to work in a fast-paced, academic environment.
- Experience with next-generation sequencing data and a background in biology are preferred but not required. A strong motivation to contribute to the development of tools for genomic research is essential.
Enter Req 2694