Postdoctoral Associate-Computational Biology/Cancer Immunology
We are looking for a post-doctoral fellow with expertise and interest in Computational Biology/Bioinformatics and Cancer Immunology to work in a research consortium studying environmental causes of breast, oral, and lung cancer. The fellow would join a multi-disciplinary team at the Boston University School of Medicine and Tufts Medical Center that focuses on the mechanisms of tumor initiation, immune checkpoint-mediated immunosuppression and how these may be affected by environmental exposures, primarily in Breast Cancer, but also in Head and Neck and Lung Cancer.
The fellow’s responsibility would include the analysis and integration of data from multiple assays, including high-throughput bulk and single cell sequencing (RNAseq, DNAseq), methylomics, and proteomics data. To this end, expert application of existing computational methodologies and development of new systems biology approaches will both be needed. The overarching goal is the elucidation of the biological mechanisms driving malignant transformation and immune checkpoint-mediated immunosuppression to improve cancer interception. Publicly available and in-house generated data from primary tissues, model organisms and 2D/3D cell cultures will be leveraged toward this goal.
Within this broad focus, there will be opportunities for the candidate to develop their own research project to support their career development.
The ideal candidate should have strong foundations in machine-learning and statistical algorithms to analyze genomic and phenotypic data from observational and experimental studies; experience in the experimental design and analysis of genomics studies; and a keen interest in following up on the biological leads the analyses will yield. The position would be for a minimum of 3 years with a possibility of 1-2 years renewal.
Qualified candidates should have:
- A Ph.D. or equivalent degree in computational biology/bioinformatics, or related field. Experience in cancer immunology is highly desirable.
- Ability to program in R/Rshiny and Python, familiarity with GitHub, Docker, and Unix systems, knowledge of database management and other programming languages a plus.
- Demonstrated biostatistics, applied bioinformatics/computational proficiency as evidenced by relevant publications in peer-reviewed journals.
- Demonstrated knowledge and use of publicly available omics data resources (TCGA, CPTAC, CCLE, GTEx, CMap, HTAN, etc.)
- Demonstrated understanding of cancer biology.
To apply: Submit an application including a statement of interest, a complete CV that includes details of training, research experiences, publications, and presentations at conferences, and contacts of 3 letter writers to David Sherr, Ph.D. (Present at AAI 2022) at email@example.com or Stefano Monti, Ph.D. at firstname.lastname@example.org.
This position will be funded through the generous donations of Find the Cause Breast Cancer Foundation