One postdoctoral position in cancer genomics are available at the Guo-Cheng Yuan Lab in the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute/Harvard School of Public Health.
The goal of the Yuan Lab is to develop computational approaches to analyze and integrate genomic data with the aim to elucidate systems-level gene regulatory mechanisms in development and disease. Current projects include inference of gene regulatory networks, cancer genomics, genome-wide chromatin state characterization, functional characterization of genetic variants, and single-cell analysis. We closely collaborate with basic biologists and medical clinicians at the Harvard Medical School to gain mechanistic insights into the stem cell biology as well as cancer and lung diseases. Detailed description of our research can be found at our group website: http://bcb.dfci.harvard.edu/~gcyuan
The candidate will work closely collaborate with clinical investigators at Dana-Farber Cancer Institute to analyze next-generation sequencing data (mainly RNAseq and ChIPseq), to identify genetic and gene expression signatures of cancer subtypes, to investigate the signaling pathways and epigenomic changes associated with cancer progression and treatment response. The candidate will also develop computational methods for integrative analysis of gene expression, DNA sequence, and epigenomic data to construct and dissect gene regulatory networks, and/or to develop new single-cell analysis approaches.
The successful applicant(s) should hold a doctoral degree or equivalent qualification in related field, such as computational biology, (bio)statistics, and computer science. Candidates holding a degree in biological/medical science are also welcome to apply if they have demonstrated experience in computational or statistical work. Strong programming (in Python, R, Matlab, or C/C++) and communication skills are required. Previous experience in analysis, interpretation, and integration of genomic data-types is required.
Lead author in at least one publication in major peer-reviewed scientific journals.
Interested applicants please send CV and at least two recommendation letters to Dr. Guo-Cheng Yuan (email@example.com).