Phatnani and Vicković Labs are seeking a dynamic, self-motivated, team-oriented Post-doctoral Research Associate to expand their efforts applying spatially resolved techniques to understand disease mechanisms. We are interested in developing integrative multi-modal data analysis tools incorporating spatially resolved omics data, imaging data, clinical phenotypes, variants and genotypes. The successful candidate will have the unique opportunity of being trained at the intersection of mathematical modeling, omics data analysis, workflow optimization, and resource building.
Responsibilities may include:
Develop new methods for integrating and analyzing spatialy resolved data in multiple modalities using current state of the art technologies;
Advise wet-lab colleagues in the design of spatialy resolved, multi-modal experiments;
Coordinate efforts across multiple organizations;
Actively participate in the preparation of manuscripts for publication and present at scientific conferences;
Assist in peer- and student-mentorship; and
Share expertise and provide training and guidance to group members as needed.
As the fellowship is shared between the two labs, the Postdoctoral Research Associate will be expected to meet regularly with Drs Phatnani and Vicković at NYGC and Columbia University's Neurology and Biomedical Engineering Departments. This individual will also interact regularly with faculty at both institutions. The fellow will participate in regular group meetings at both NYGC and Columbia, and will participate in meetings of NYC-wide working groups.
About the Phatnani Lab
Dr. Hemali Phatnani holds a joint faculty appointment in Columbia University's Department of Neurology and leads the Center for Genomics of Neurodegenerative Disease (CGND) at the New York Genome Center. Dr. Phatnani's lab is dedicated to the study of neurodegenerative diseases such as ALS and Dementia. Currently, our group received funding to engage in a groundbreaking collaboration with multiple departments in which we use the integration of Spatial Transcriptomics (ST), single-nucleus RNA-seq (snRNA-seq) data, and Iterative Indirect Immunofluorescence Imaging (4i)-based proteomic profiling that will enable to generate maps of known and novel senescence-associated markers, senescent cells, and the effects of senescent cells on their surroundings in each tissue type across the human lifespan.
The CGND at NYGC is the hub of a worldwide collaborative research effort centered on the genetics and genomics of ALS and FTD. The Phatnani lab uses Spatial Transcriptomics (ST) to deconvolve both spatial and cell-type specific gene expression in the central nervous system; simultaneously measuring gene expression changes across entire brain or spinal cord regions from human tissue while retaining spatial information. In conjunction with such transcriptome-wide approaches, the Phatnani and Vickovic labs also developed tools and workflows to enable highly-multiplexed, high-throughput spatially-resolved immunofluorescence measurements on human tissue. The lab partners with several clinical centers within and outside the United States to apply these methods to the study of human postmortem tissue.
About the Vicković Lab
The Vicković Lab is at the New York Genome Center, Columbia University's Fu Foundation School of Engineering and Applied Science and Columbia University's Herbert and Florence Irving Institute for Cancer Dynamics.
The Lab is focused on developing novel "digital pathology" tools to track disease progression with the aim of identifying translatable drug and therapeutic targets in human tissue cohorts. The team leverages technologies including spatial transcriptomics, single-cell sequencing, and machine learning. A particular area of disease focus for the lab is developing tools for spatial genomics that will enable perturbing highly complex interactions in the human gut. The team is studying why what happens to our body during aging. A few examples of current and past areas of research include Spatial Transcriptomics and Spatial Multi-Omics, ECCITE-seq, Cell Hashing and CITE-seq.
A minimum of a Ph.D. in computational biology, mathematics, or related discipline required;
In-depth understanding of modern high throughput technologies in the life sciences required;
Prior experience with bayesian inference, network inference and image analysis, are preferred, but exceptionally creative and driven scientists with an interest will be seriously considered;
Strong oral and written communication, data documentation, and presentation skills required;
Ability to handle multiple projects at the same time required; and,
Excellent collaborative and interpersonal skills required.
Columbia University is an Equal Opportunity Employer / Disability / Veteran
Pay Transparency Disclosure
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.
Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis. It seeks to attract a diverse and international faculty and student body, to support research and teaching on global issues, and to create academic relationships with many countries and regions. It expects all areas of the university to advance knowledge and learning at the highest level and to convey the products of its efforts to the world.