The laboratory headed by Ajay Goel, PhD focuses on understanding the interplay between genetic and epigenetic alterations in colorectal, pancreatic, gastric and esophageal cancers. We are a multidisciplinary research group, undertaking a number of collaborative projects involving large international consortia.  The projects will involve any/all of the following research focuses.

• Exploration of the role of aberrant DNA methylation, histone modifications and non-coding RNA’s (miRNA, lncRNAs, snoRNAs, piRNAs etc.) in gastrointestinal cancers.
• Establishment of innovative methodologies for the development of epigenetic and non-coding RNA’s-based biomarkers for the earlier diagnosis, prognosis and predictive responses to chemotherapy in gastrointestinal neoplasia.
• A better understanding of the translational and clinical role of genetic and epigenetic signatures in gastrointestinal cancers.
• Understanding the genetic basis of early-onset and familial colorectal cancers.
• Chemoprevention of gastrointestinal cancers using dietary nutraceuticals, non-steroidal anti-inflammatory drugs (NSAIDs), and other drugs.
• Molecular mechanisms of cancer chemotherapy, stem cells, chemo-resistance and chemo-sensitization.
• Gut microbiome, and its influence on gastrointestinal cancers.

The position is largely computational but involves substantial collaboration with wet-lab researchers providing data and is focused on fundamental biology.  This interdisciplinary position offers opportunities to address important questions in computational biology and cancer genomics with first access to large novel data sets from various genomic/epigenomic platforms.  We have a strong preference for bioinformaticians/computational biologists with a deep appreciation of biological phenomena or, alternatively, experimentalists with a solid background in bioinformatics/computational biology analysis of transcriptomic and genomic data with regard to detection of differentially expressed genes, noncoding RNA’s, DNA methylation and differential processing profiles.  

This individual will also have the opportunity to develop original statistical and machine learning methodologies and analytical and computational tools to extract knowledge from complex and big data. 

The ideal candidate will possess:

• An excellent track record of analyzing next generation sequencing data in cancer, particularly whole genome, RNASeq and methylation sequencing analysis including:
• Differential expression analysis, SNV calling, indel calling, rearrangement/translocation calling, CNV identification and analysis.

• A good knowledge and understanding of cancer biology
• Passion for science and reproducible scientific work
• Proficient in spoken and written English
• Being comfortable in interacting with colleagues in an interdisciplinary setting