We are always on a lookout for talented individuals who share our passion of functional genomics and would like to work in a highly collaborative and dynamic environment. Currently, we are seeking qualified postdoctoral candidates for both computational and wetlab positions. To apply, please send your cover letter, CV, and contact information for at least two references to peter.kharchenko@post.harvard.edu.

Statistical analysis of single-cell data

Rapid progress of single-cell genomic analysis methods, including spatially-resolved approaches, is changing our understanding of biology. In particular it allows for a more probabilistic view of how individual cells, and complex biological tissues are organized. The interpretation of such massive data, however, depends on intensive computational analysis that separate biologically significant signals from noise, and capture higher-level patterns in the data. This is a dynamic field, as new assays being developed by our collaborators as well as other groups, and increasing availability of large-scale datasets, require new statistical methods and algorithms for analysis.

Much of the work will be conducted in close cooperation with our experimental collaborators, on projects requiring integrative analysis of epigenetic, transcriptional and spatially-resolved data in the context of specific biological processes (e.g. cell differentiation, dosage compensation, etc). Many projects are formulated in the context of NIH HuBMAP and BRAIN consortia. The candidate will also work on development and deployment of novel computational tools, aimed for instance, at comparative analysis of extensive single-cell dataset collections, spatialy-resolved data, genetic or phenotypic data. Candidates with interest in biological/molecular microscopy image analysis are encouraged to apply.


  • A strong interest in conducting collaborative research on the topics outlined above is paramount.
  • PhD degree in Computational Biology, Bioinformatics, Biophysics, or a another quantitative discipline.
  • Research track record.
  • Expertise in statistical methods (e.g. Bayesian statistics, expectation-maximization) and algorithm development in the context of biological systems.
  • Experience in analysis of functional sequencing data, such as RNA-seq or ATAC-seq is advantageous.
  • Proficiency in R and Bioconductor. Knowledge of C/C++ and Java, as well as general proficiency with UNIX operating systems is strongly desired.
  • Good communication skills.

Microfluidic devices for cellular assays

While modern sequencing-based assays can be remarkably informative, their application to investigations of specific cellular structures or rare cell types is typically limited by the variability of sample processing and the amount of labor required to examine sufficient number of samples. We are aiming to bridge this gap by performing key aspects of the protocol in computer-controlled microfluidic devices.

The researcher will be tasked with developing and applying custom microfluidic designs for genome-wide analysis of gene expression, epigenetic marks and other genomic properties of small samples or single cells. We hope to apply these methods to investigate cellular phenotypes in the context of human cancers and other diseases.


  • Excellent expertise in developing microfluidic devices for cellular assays.
  • Strong interest in conducting collaborative research on regulation of mammalian tissues at a molecular level.
  • Interest in next-generation sequencing assays.
  • PhD degree in Engineering, Biophysics, or a related discipline.
  • Track record of publications in peer-reviewed journals.