(Check the full job description and application page at the following link: http://m.rfer.us/STANFORDlqTESA)
The Department of Medicine, Division of Cardiovascular Medicine is looking for a Bioinformatics Engineer to join the Bioinformatics Core (BIC) of the Molecular Transducers of Physical Activity Consortium (MoTrPAC) tasked with building a molecular map of physical activity (MoTrPAC - https://commonfund.nih.gov/moleculartransducers/overview). The Bioinformatics Core is supervised by co-PIs Dr. Euan Ashley and Dr. Matthew Wheeler in the Division of Cardiovascular Medicine at Stanford University. Dr. Ashley’s research focuses on leveraging emerging technologies such as genomics and wearable sensors to provide insights into precision medicine (https://ashleylab.stanford.edu/). Dr. Wheeler’s research focuses on genomics, rare myocardial and skeletal muscle disease, and undiagnosed diseases (https://undiagnosed.stanford.edu). Group members have opportunities for cross-disciplinary collaboration and engagement with clinical, translational, wet lab, and dry lab researchers.
The BIC’s goal is to study the molecular changes that occur during and after exercise to advance the understanding of how physical activity improves and preserves health. This incredible project integrates very large volumes of clinical and densely time sampled molecular data (genomic, epigenomic, transcriptomic, proteomic and metabolomic data). The bioinformatics core is building cutting-edge infrastructure to manage, analyze and disseminate this resource to the research community through the Google Cloud Platform. Our portal (https://motrpac-data.org) will push the boundaries of biomedical data analytics to provide insight into the basic and translational science of exercise.
We are seeking a highly talented and motivated Bioinformatics Engineer to support our pipeline and data analysis team. Your role will focus on the development of pipelines and tools for the comprehensive analysis of large amounts of molecular and clinical data being generated by the consortium, with special emphasis on Metabolomics and Proteomics datasets. Your ability to understand experimental designs, and strong experience in the analysis of MS-based molecular and/or next-generation sequencing datasets, proficiency with programming languages, demonstrable exposure to high-performance computing environments and ability to participate in software development projects, will be a key resource in enabling high-quality data to flow through our systems to enable analytical insights.