Description

The Dana-Farber/Boston Children’s Cancer and Blood Disorders Center is seeking an experienced computational immunology PhD to join our Division as an Instructor in Pediatrics.  This position is focused on investigating the immunology of complex preclinical and clinical systems with relevance to hematopoietic stem cell transplant (HCT) and cellular therapies.  The successful applicant will play a key role in the development and implementation of new analysis pipelines, driving thoughtful experimental designs, analyses and inferences throughout the field of HCT and cancer immunotherapy. 

We are looking for a talented person with extensive experience in NGS data analysis, proficiency in R and Python, experience in the application of machine learning methods, and specific expertise in computational immunology. As a part of this position the successful candidate will collaborate closely with members of the Division of Hematology/Oncology, the Dana-Farber/Boston Children’s Cancer and Blood Disorders faculty and mentor research associates, graduate students, and post-doctoral fellows in computational immunology techniques.  This position is considered a pathway to independence, and the successful candidate is expected to obtain grants, demonstrate continued research productivity and initiate an independent job search within 2 years of accepting the position.  

Experience with scRNA-Seq, ATAC-seq, deep TCR sequencing analysis in mouse models, non-human primates, and human data is required. The successful applicant will focus on developing novel integrative analysis strategies for large “omic” data sets, including the above.

Requirements

Position Requirements:

   PhD in bioinformatics, computational biology, genetics, statistics or equivalent experience; alternatively, a PhD in molecular biology, immunology, or cancer biology with a very strong publication record demonstrating bioinformatics expertise and the ability to perform high throughput data analyses

   Experience analyzing NGS sequencing data and familiarity with genomics data processing pipelines

   Strong background in statistical analysis of genomic/high-throughput sequencing data

   Experience in data analysis, interpretation, and effective visualization of large datasets

   Fluency in R, and Python with an emphasis on NGS analysis

   Collaborative development skills using version control systems (e.g. Git)

   Experience with common databases and genome browsers (e.g., Ensembl, IGV, UCSC)

   Ability to work independently and as a member of a multifunctional analytical team in a fast-paced environment

   Strong organizational and interpersonal, verbal, and written communications skills

   Domain knowledge in immunology and cancer biology and relevant experimental techniques (e.g., flow cytometry, immune sequencing, RNA-seq, ATAC-seq)