The successful candidate will provide bioinformatic support as a Staff Scientist for the research program of Dr. Ludmila Prokunina-Olsson, LTG Chief and Senior Investigator. Her research program is focused on genetics and genomics of bladder cancer and the role of IFNL4, a type III interferon, in infection and cancer. Specifically, this support will include the following activities: accessing, extracting and preparing data for analysis, developing and maintaining bioinformatics pipelines, conducting analysis of genetic and molecular epidemiology data within and across omic platforms including use of integrative analytic methods, organizing results into clear presentations and concise summaries of work, and maintaining experience with state-of-the-art bioinformatics tools and data repositories. The candidate will also participate in mentoring and training next generation of scientists.

The successful candidate must hold a doctoral degree in genetics, genomics, bioinformatics, biostatistics, computer science, computational biology or other related disciplines. The ability to communicate effectively in speech and in writing is important, as demonstrated by a track record of publications in peer-reviewed literature as part of a research team. The successful applicant will possess many (although not necessarily all) of the following skills: the ability to program efficiently in at least one programming language (e.g., Python, Perl, C/C++, and/or JAVA); experience with processing and analyzing large datasets for at least one of the following: GWAS, transcriptomics, methylomics, metabolomics, microbiomics, next generation sequencing; experience with publicly software available through GitHub and other sources; proficiency in R/Bioconductor; proficiency with public bioinformatics databases (e.g., UCSC Genome Browser, TCGA, ENCODE, 1000 Genomes, dbGAP, GTEX, SRA NCBI); proficiency with bash scripting and working in a Linux environment (especially a computer cluster environment); proficiency with core statistical and bioinformatics methods (e.g. linear regression, logistic regression, eQTL analysis, LDscore regression, credible set and colocalization analysis, etc.); and a demonstrated ability to self-educate in current and evolving bioinformatics techniques and resources. The successful candidate will conduct/supervise the analysis of data generated by functional genomic methods such as CRISPR and MPRA screens, chromatin interaction and splicing assays.