Apply on the Stanford Careers Site: https://stanford.taleo.net/careersection/jobdetail.ftl?job=77679&lang=en
The Department of Medicine, Division of Cardiovascular Medicine is looking for a Computational Biologist to join Dr. Euan Ashley's team 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/).
Our team runs the Bioinformatics core for a large NIH study (>$180M) tasked with building a molecular map of physical activity (MoTrPAC - https://commonfund.nih.gov/moleculartransducers/overview). The program'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 will integrate very large volumes of clinical (imaging, EKG, wearable) and densely time sampled molecular data (DNA, RNA, ATAC, EPI sequencing, metabolomics, proteomics and the microbiome). The bioinformatics core will build cutting edge infrastructure to manage, analyze and disseminate this resource to the research community through the Google Cloud Platform. Our portal will push the boundaries of biomedical data analytics to provide insight on basic research and translational science such as the developments of new therapeutics.
The role of Computational Biologist will focus on modeling of the large amounts of molecular and clinical data being generated by the consortium. You will work with stakeholders across the network to best understand data structures to model metadata schemas and be the point person for data transactions to the Bioinformatics core. Your understanding of molecular data and experimental design will be a key resource in enabling high quality data to flow into our systems to enable analytical insights.
- Graduate degrees that emphasize engineering, computer science and statistics are preferred
- Domain expertise in at least two omics such as WGS, RNA-Seq, RRBS, proteomics, metabolomics, microbiome
- An understanding of data modeling including ontologies and database design
- Experience with Python and Linux bash scripting
- Experience with experimental design protocols
- Basic knowledge of code management such as git and Jenkins
- Exposure to data analytics toolsets such as R, SciPy/NumPy, Matlab
- Exposure to cloud computing through Amazon, Google
- Exposure to container systems such as setting up virtual machines and Docker instances