Post-doctoral or Master’s level position available at Dana-Farber Cancer Institute/Harvard Medical School for a highly motivated, hard-working, independent individual in computational biology.  The successful candidate will develop and apply computational solutions for integrative analysis of gene expression, RNAseq, and ChIPseq data, to gain mechanistic insights into the effect of drug treatment in altering the transcription factor activities, epigenetic patterns, and pathway activities in mammalian cells. He/she should be able to work simultaneously on multiple projects, involving a diverse and interdisciplinary team of scientists across laboratories. The position will be jointly co-mentored by Dr Rani George (Dana-Farber Cancer Institute) and Dr Karen Adelman (Biological Chemistry and Molecular Pharmacology). 

The successful candidate will develop and apply computational solutions for integrative analysis of gene expression, RNAseq, and ChIPseq data, to gain mechanistic insights into the effect of drug treatment in altering the transcription factor activities, epigenetic patterns, and pathway activities in mammalian cells

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.

The ideal candidate should be a PhD/MS in computational biology, (bio)statistics, computer science, or a similar field with experience in the analysis of high throughput data. Candidates holding a degree in biological/medical science are also welcome to apply if they have demonstrated strong 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 RNAseq and ChIPseq data is also required.  

Lead author in at least one publication in major peer-reviewed scientific journals.