Positions Available:
We are currently have openings for
  • Postdoctoral fellows
  • Graduate students (Columbia students only)
  • Undergraduate students
  • Staff programmer

Exceptional candidates are welcome to contact Dana Pe'er for more information about our research projects. In order to join the lab as a PhD student, you must first gain admittance to one of the graduate programs at Columbia University. Please e-mail a brief description of your research interests, background and CV to Dana Pe'er .

Postdoctoral Positions:
  • Computational Biology and Machine Learning: Positions available for computational biologists with strong machine learning skill and desire to have a significant impact on biomedical research. The project involves the development of new computational algorithms and statistical approaches that integrate diverse types of high-throughput data towards modeling and understanding patient specific cancer networks and their response to drug, a step towards personalized medicine. We have access to unique datasets that measure genetics, genomics and single cell proteomics of individual cancers, following drug and other perturbations (Pe'er and HaCohen, Cell March 2011, Bendall et.el., Science May 2011). Our lab is an interdisciplinary environment, the computational postdoc will participate in the experimental design of the data, work side by side with biological and clinical collaborators and will have opportunity for biological validation of model predictions. The ideal candidate would have prior training in both machine learning and computational biology. Exceptional candidates with strong background in only one of machine learning, theoretical physic with strong programming abilities or computational biology will be considered.

  • Genomic Cancer Biology: Position available for researcher with extensive knowledge and background in cancer genomics or systems biology of signaling. Projects involve using high-throughput technologies to collect genomic and proteomic data probing tumor networks, developing novel tools and using available in-house tools to analyze this data integrated with additional available data (TCGA, ENCODE) to identify driver mutations, learn how these alter cell signal processing and arm a cell with the abilities to proliferate abnormally and evade drugs. The ideal candidate would have training in cancer genomics, genetics or bioengineering, with some computational programming background. Exceptional candidates with similar backgrounds will be considered.