POSTDOCTORAL SCHOLAR, Division of Computational Biomedicine (9876/J1217)
Joshua D. Campbell is an assistant professor in the Division of Computational Biomedicine, a member of the BU-BMC Cancer Center, and an affiliate member of the Broad Institute of MIT and Harvard. The focus of the Campbell lab is to develop and apply computational methods for high-throughput genomic technologies such as DNA and RNA sequencing to understand and characterize a variety of biological systems and diseases including cancer initiation and progression, mutational burden of carcinogens, the response to cigarette smoke, and lung development.
We are looking for a postdoctoral scholar to fill one of the following categories:
1) Statistical modeling and development. We are developing novel discrete Bayesian hierarchical models to characterize cell populations and transcriptional states in single-cell RNA-seq data and mutational signatures in cancer genomic data. Experience with the development and implementation of discrete Bayesian hierarchical models such as those commonly used in the “topic modeling” field would be strongly preferred. Candidates with experience in developing of other types of Bayesian hierarchical models will also be considered.
2) Computational Biology and Bioinformatics: We are working with various NGS data including single cell RNA sequencing, whole exome/genome DNA sequencing, and/or bulk RNA sequencing in projects related to genomic characterization of lung premalignant lesions in the “Pre-Cancer Genome Atlas” consortium funded by Stand Up 2 Cancer (SU2C). Experience with cancer genome analysis from DNA sequencing data or expression analysis from RNA sequencing data would be strongly preferred.
Collaborations and research networks:
We work closely with other labs in the section of Computational Biomedicine that have a wide range of expertise in areas such as clinical medicine, computational biology, and biostatistics. We also maintain strong relationships around the greater Boston area including labs at the Broad Institute, Dana-Farber Cancer Institute, and Harvard Medical School. You will have the opportunity to work with people from within BU and from these other institutions to expand your academic network. In addition to your own projects, you will have access to multiple methods development and analysis working groups to increase knowledge and proficiency of genomic algorithms and approaches. Training in grant writing will be provided by faculty and university-sponsored workshops. While funding for this position is available through 2019, independent fellowships will also be encouraged.
- Ph.D. or equivalent degree in bioinformatics, computational biology, biostatistics, statistics, or a related field within the past 5 years.
- Excellent communication skills in both spoken and written English are required.
- Excellent critical thinking and problem-solving abilities are required.
- Experience developing or applying algorithms for analyzing large-scale genomic datasets is preferred.
- Experience with Unix/Linux is required and experience with R and python is preferred.