Postdoctoral Research Fellow - Michor Lab

Job Details

Job ID:

450 Brookline Ave, Boston, MA 02215


Employment Type:
Full time

Work Location:
Hybrid: 2-3 days onsite/week


Postdoctoral Research Fellow will work on independent research projects in computational cancer evolution directed by the lab’s Principal Investigator, Franziska Michor, PhD. ( The lab works on developing and validating computational biology and mathematical modeling strategies of tumor evolution, heterogeneity and treatment response, and eventually utilize these strategies to identify best therapeutic interventions. We investigate the treatment response of cancer cells and their microenvironment, develop novel computational approaches and mathematical frameworks describing the evolutionary dynamics of tumor progression and treatment response that are parameterized using the experimental systems and clinical data, and predict and validate optimum intervention strategies that will ultimately be implemented as prospective clinical trials. We also use bioinformatics pipelines and develop novel bioinformatics approaches to analyze data types such as single cell RNA sequencing data, spatial transcriptomics data, epigenetics data such as DNA methylation and ATAC sequencing data, and others. The lab is looking for a highly motivated and qualified Postdoctoral Research Fellow to investigate tumor evolution and heterogeneity using and creating bioinformatics approaches and/or mathematical models of tumor evolution and drug resistance in order to further the understanding of cancer evolution, metastasis formation, and emerging drug resistance.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.


  • Perform methodological and/or collaborative research under the direction of the lab’s PI
  • Design and carry out analyses of datasets
  • Work directly with “bench” scientists in analyzing data generated from individual projects
  • Prepare and deliver presentations as needed
  • Interact with other members of the lab as well as the lab’s scientific collaborators
  • Assist in grant and manuscript preparation


  • PhD, MD, PhD-MD or doctoral equivalent degree.
  • Background in applied or pure mathematics, statistics, bioinformatics, machine learning or computational biology; interest in cancer biology and work experience in computational biology and/or biostatistics are preferred
  • Knowledge of mathematical modeling and evolutionary theory as related to cancer, and/or knowledge of biostatistical methods and bioinformatics software and the ability to create novel analysis platforms desired
  • Experience in statistical computing, preferably in a biological research setting
  • Familiarity with multiple computing platforms and computer languages
  • Ability to work in a team setting
  • Willingness to work variable hours and days

At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong.  As relentless as we are in our mission to reduce the burden of cancer for all, we are equally committed to diversifying our faculty and staff.  Cancer knows no boundaries and when it comes to hiring the most dedicated and diverse professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

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 characteristics protected by law.

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