The Kleinstein Lab (Program in Computational Biology & Bioinformatics; Yale School of Medicine, Department of Pathology) and McDougal Lab (Program in Computational Biology & Bioinformatics; Yale School of Public Health, Department of Biostatistics Division of Health Informatics) are seeking a highly motivated postdoctoral associate to join the NIH-funded Human Immunology Project Consortium (HIPC; www.immunespace.org) as a member of the HIPC Data Coordinating Center. The research will focus on the development and application of methods to capture published immune signatures of infection and vaccination in a machine-readable format. This will include: (1) engaging the 8 current national HIPC centers to share emerging research results, (2) automating the identification of papers to target for signature capture, and (3) developing natural language processing and information extraction approaches to identify and validate immune signature components (cohorts, immune exposure, response, etc). Involvement in systems immunology analysis projects to identify and compare signatures is possible for interested candidates.
The Kleinstein Lab (medicine.yale.edu/lab/kleinstein) pairs big data analyses with immunological domain expertise to better understand how the dynamic processes of the immune system affect the course of infection, vaccination and autoimmunity. The lab has developed many widely used bioinformatics analysis methods for high-throughput immune profiling data, particularly transcriptomic and B cell receptor repertoire sequencing data (medicine.yale.edu/lab/kleinstein/software). Dr. Kleinstein is MPI of the HIPC Data Coordinating Center.
The McDougal Lab develops and applies informatics and simulation approaches to gain insight into multiscale biomedical phenomena. They are especially interested in deriving information from scientific artifacts (computational models and papers) and in improving the practice, rigor, and capabilities of simulation studies. The lab is a major contributor to the NEURON simulator (nrn.readthedocs.io) and to ModelDB (modeldb.science), a discovery tool for computational neuroscience models.
The ideal candidate will have strong quantitative and programming abilities (ideally Python, MongoDB, and web technologies), along with an interest in applying these skills to problems in immunology. A PhD in a quantitative discipline (Bioinformatics, Computer Science, Applied Mathematics, Statistics, etc) and prior experience with information extraction is preferred.
Interested candidates should send a CV and cover letter to: steven.kleinstein@yale.edu