A post-doctoral position in human computational genomics/transcriptomics is available at the Center for Translational and Computational Neuroimmunology at Columbia University Medical Center in New York, NY (https://www.columbiactcn.org).
We are looking for an enthusiastic and energetic individual to join our research effort investigating the functional consequences of human genetic and transcriptomic variation in the aging brain. Specifically, we are building multi-modal network models of neurodegenerative disease and aging-related cognitive decline based on single-cell RNA-sequencing and spatial transcriptomics, complemented by large-scale genetics, epigenetics, and proteomics data.
The candidate will be analyzing single-cell RNA-sequencing data from human and mouse brains covering multiple regions and neurodegenerative conditions, and relating findings to whole genome sequencing, proteomic, and epigenetic data. In addition, the candidate will be involved in synthesizing spatial transcriptomics data to localize cell types and changes in tissue. The ultimate goal is to build networks of gene-gene and cell-type interactions, and assess their dysregulation in Alzheimer’s, Multiple Sclerosis, and other diseases observed in aging brains. In addition, there is a concurrent effort to profile major subclasses of cells in higher detail across these modalities, which will provide additional data to refine the gene- and cell-interaction networks. The candidate will be responsible for designing, leading, and carrying out integrative analyses of large sets of single-cell and single-nucleus RNA-sequencing data, and developing innovative methods to integrate this with complementary genomics data.
The ideal candidate should have a quantitative background (Statistics, Biostatistics, Computer Science, Computational Biology, Applied Mathematics or Bioinformatics), be highly motivated to solve biological problems, and have experience analyzing large-scale and high-throughput genomics data, especially RNA-seq. The candidate should have good knowledge of one programming language for implementing computational models and algorithms on large-scale data (R is preferred, Matlab, Python, Perl, C/C++ also options). Experience with graph theory and network analysis is a plus. Because our work involves multiple collaborators, a good balance between independence and team spirit is essential, and the ability to communicate effectively and in a timely manner are necessary.
The successful candidate will be part of an integrated team of data scientists, cellular biologists, human immunologists, and clinician scientists who come together to perform team-based projects, with each post-doctoral fellow leading his or her own project.
The position therefore offers a stimulating and multi-disciplinary environment and the opportunity to work with researchers at Columbia University, the New York Genome Center, and the Broad Institute of MIT and Harvard University. There will be many opportunities to contribute to multiple ongoing national and international collaborative projects.
Application and inquiries should be submitted by e-mail to Vilas Menon (email@example.com). Along with your CV, please include a cover letter describing previous research, research interests, and future goals. Please provide contact details for 3 references.
PRINCIPAL DUTIES AND RESPONSIBILITIES:
1. Responsible for the design, execution, and interpretation of planned analyses
2. Contribute to QA/QC of data to prepare datasets for analysis.
3. Interface with team to accomplish group projects in addition to primary project.
4. Interface with collaborators to prepare, analyze, and interpret data.
5. Display initiative and independence in accomplishing all duties and responsibilities
6. Prepare summary reports of data and results for dissemination to colleagues and collaborators. Both technical summaries and documents for a wider scientific audience will be generated.
7. Participate in grant writing and proposals as needed.
8. Directly respond to inquiries regarding projects being managed. Produces subsets of data for distribution to collaborators as approved by the principal investigators.
Requirements: Ph.D. in computational biology, applied mathematics, statistics, biostatistics, physics, computer science, or related area. Ph.D. in biology or neuroscience with extensive quantitative and programming experience is also acceptable.
• Demonstrated analytical skills
• Outstanding problem solving skills
• Strong organizational skills in managing large datasets
• Experience parsing and analyzing RNA-sequencing data
• programming experience in R, Matlab, Python, Perl, or C/C++
• ability to work independently and display initiative within a team environment
The candidate may supervise rotating students as opportunities arise.