A Lineberger Comprehensive Cancer Center Research Laboratory aims to understand the pathogenesis and pharmacogenomics of pancreatic cancer in order to accelerate its treatment. Using a combination of molecular and genomic data, and human in mouse models, we have identified four core subtypes in pancreatic cancer with important biologic and clinical implications. We are seeking a postdoctoral associate in cancer genomics and personalized medicine who will be responsible for computational method development, data analysis, and experimental design. The qualified candidate will integrate information from a combination of sources, including high throughput screens, proteomic, and next-gen sequencing data in order to generate and validate hypotheses of therapeutic relevance to pancreatic cancer. The ideal candidate will have excellent qualifications in a quantitative/computational field with a strong interest in learning cancer biology and/or wet bench techniques. Candidates from a largely wet bench background with a Ph.D. may be considered if the quantitative/computational background is excellent.  

To be successful, the candidate must have experience in and interested in developing several of the following skill-sets: -Thorough knowledge of cancer biology, epigenetics, proteomics, and transcriptomics -Familiarity with next-generation sequencing data analysis tools -Experience working in Linux environments, including batch job management on shared computing resources -Familiarity with a variety of supervised and unsupervised classification techniques -Proficiency in one or more statistical or scripting languages, preferably R or MATLAB -Knowledge of survival-based statistical analysis, e.g. Cox regression and Kaplan-Meier analysis -Working knowledge of best-practices for machine learning to avoid over fitting -Familiarity with common experimental techniques in molecular biology -Ability to communicate scientific material and collaborate well with computational and non-computational partners -Excellent oral and written communication skills and the ability to perform both self-directed and guided research -Outstanding personal initiative and the ability to work effectively as part of a team -Willingness to assist in the mentorship and training of pre-doctoral researchers Ph.D. in a quantitative/computational field or a Ph.D. with an equivalent certificate in a quantitative/computational field is required. -Have experience in handling large datasets and have applied/developed computational algorithms in the context of molecular biology. -Have a thorough understanding of common statistical tests and distributions. 

Be able to collaborate with experimental team members for validation of computational results. -Be comfortable maintaining datasets, as well as displaying and interpreting processed data for publication. -Work alongside domain experts in the optimization and development of experimental measurement platforms and protocols.