Multiple (5+) postdoctoral positions for wet (experimental), dry (computational), or hybrid biologists are available in the lab of Dr. Golnaz Vahedi at the University of Pennsylvania. The Vahedi lab’s productivity (22 publications in 5 years) has been recognized by prestigious NIH and foundation awards. We are supported by the Burroughs Wellcome Fund, Chan Zuckerberg Initiative, the Sloan Foundation, and the National Institutes of Health including 4D Nucleome and HIRN Consortiums.
We are affiliated with the Department of Genetics, the Epigenetics Institute, and the Institute for Immunology, Perelman School of Medicine at the University of Pennsylvania. The Vahedi lab is multidisciplinary, integrating cutting-edge experimental and computational approaches to develop a mechanistic understanding of cell fate determination in the immune system. Further information about our science could be found at vahedilab.com/
Our major contributions to the field include:
(1) Discovery of genome misfolding in type 1 diabetes (www.cell.com/immunity/fulltext/S1074-7613(20)30030-3 received major media attention and featured on journal’s cover)
(2) Discovery of TCF-1 as a pioneer factor in T cells (www.cell.com/immunity/fulltext/S1074-7613(18)30033-5 featured on journal’s cover)
(3) Development of a novel technology for the joint profiling of chromatin accessibility and CAR-T integration site analysis at the single-cell level (www.pnas.org/content/early/2020/02/19/1919259117.short)
Experimental candidates are required to hold a PhD in biomedical sciences and track-records in molecular biology, immunology, or biochemistry. Experience with latest genomics and/or imaging technologies will be viewed favorably. Potential training opportunities in computational sciences exist for the experimental trainees.
Computational candidates are required to hold a PhD in computer science, engineering, physics, or related quantitative programs. Expertise with the Unix environment and programing in Python and R are required. Trainees with track-records in machine learning or statistics are encouraged to apply.