Looking for a highly motivated individual to join the Cahan Laboratory (http://www.cahanlab.org) in the Department of Biomedical Engineering and the Institute for Cell Engineering at Johns Hopkins University School of Medicine. A major aim of our research is to develop computational and experimental tools to assess the fidelity of cell populations derived from embryonic and induced pluripotent stem cells. Towards this end, we work across several disciplines including single-cell genomics, computational biology, and molecular and developmental biology. The applicant will work closely with the faculty investigator to lay the computational foundation of the new lab and will be expected to contribute to the intellectual output of the laboratory. This position will entail a lot of programming (mainly in R), ‘Big Data’ analytics, and the opportunity to collaborate and develop connections both within the Johns Hopkins community and beyond. This is an ideal position for a recent undergraduate with a quantitative or computational degree seeking practical experience in an academic research setting prior to attending graduate school in Bioinformatics, Computational Biology, or Systems Biology.
Duties and responsibilities, under the supervision of the faculty investigator, include:
- Developing and applying network biology analytics to single cell genomics data
- Engineering robust, reusable, and extensible analytical software tools for bioinformatics applications
- Harvesting and integrating publicly available NGS data
- Prioritizing and juggling multiple, diverse projects
- Communicating effectively and promptly with collaborators
- Presenting results to lab members and to collaborators
- Educating students and postdoctoral fellows in the lab how to use our computational resources, and our conventions for code development
- Extending, customizing, optimizing, and supporting the lab’s code base.
- Adding front-end interfaces to our computational tools
- Devising and implementing new algorithms
- Assessing bioinformatic tools by comparison to gold standards
- Keeping up to date on developments in the field
- Adapting and applying analytical pipelines for collaborations