Posted April 7, 2021
POSTDOCTORAL ASSOCIATE, Boston University School of Medicine, Computational Biomedicine
- The focus of the Section of Computational Biomedicine (https://www.bumc.bu.edu/compbiomed/) at Boston University School of Medicine (BUSM) is to develop and apply computational methods for high-throughput genomic technologies to understand and characterize a variety of biological systems and diseases. Several labs within the section collaborate and have joint research interests in the lung to understand the molecular alterations associated with cigarette smoke in the airway and lung cancer initiation and progression. We are looking for a postdoctoral associate to develop methods to analyze NGS data including single-cell RNA sequencing and CITE-seq, whole exome/genome DNA sequencing, and bulk RNA sequencing in projects related to the genomic characterization of lung premalignant lesions in the “Pre-Cancer Genome Atlas” consortium funded by Human Tumor Atlas Network https://humantumoratlas.org/hta3/). Therefore, data analysis and method development experience with single-cell RNA-seq, DNA-seq data, and/or RNA-seq data in the setting of cancer is preferred.
This position will be supervised by Drs. Jennifer Beane and Joshua Campbell and collaborate very closely with the Mazzilli and Spira/Lenburg Computational Biomedicine labs. The position will involve working with several investigators and trainees with a wide range of expertise in areas such as clinical medicine, computational biology, functional genomics, and biostatistics. In addition, the postdoctoral associate will have access to and participate in 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 supervising faculty and also available through university-sponsored workshops. Funding for this position is available through 2022 however independent fellowships will be encouraged and supported.
- Required Skills
- 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.
DO NOT APPLY THROUGH THE BU WEBSITE:
Please email a cover letter and CV to Dr. Joshua Campbell (firstname.lastname@example.org).
Posted October 21, 2019
Dr. Joshua D. Campbell is an assistant professor in the Division of Computational Biomedicine (CBM) in the Department of Medicine at Boston University School of Medicine. He is also a member of the BU-BMC Cancer Center, and an affiliate member of the Broad Institute of MIT and Harvard. The overall focus of our group is to develop and apply computational methods for single cell genomic technologies 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.
Dr. Masanao Yajima is an associate professor of the practice in the Department of Mathematics & Statistics. His research has revolved around development of methods and tools for analysis of biomedical and bioinformatics research and social science. For example, he developed the R package MAST which is gaining support among leading research institutions for the analysis of single cell RNA data as well as “mi” which is one of most popular R packages for missing data imputation in social science. He currently helps to run the MS in Statistical Practice (MSSP) program in the Department of Mathematics & Statistics at Boston University which has supervised successful inter-disciplinary consulting and collaborative projects in a variety of fields, including bioinformatics, biology, epidemiology, marketing, psychology, forensic anthropology, and social work.
Single-cell genomic technologies such as single-cell RNA-seq have emerged as powerful techniques to quantify molecular states of individual cells and can be used to elucidate the cellular building blocks of complex tissues and diseases. Given recent rapid advances in single-cell technologies, novel statistical and computational approaches are needed to efficiently analyze large-scale single-cell datasets with multiple data types such as gene and protein expression. Discrete Bayesian hierarchical models have been widely used for unsupervised modeling of discrete data types in fields such as Nature Language Processing (NLP). We have developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) which can perform bi-clustering of genes into modules and cells into subpopulations. This position will be focused on developing novel models such as those that can perform clustering of cells into subpopulations using multi-modal genomic data.
This position is funded through an R01 from the National Library of Medicine. However, independent fellowships will also be encouraged.
Collaborations and research networks:
We work closely with other labs in CBM that have a wide range of expertise in clinical medicine, computational biology, biostatistics, and computer science. We maintain strong relationships around the greater Boston area including labs at Dana-Farber Cancer Institute, Harvard Medical School, and the Broad Institute. You will have the opportunity to work with people from within BU and from these other institutions to expand your academic network. Training in grant writing will be provided by faculty and university-sponsored workshops.
– Ph.D. or equivalent degree in statistics, computer science, electrical engineering or a related field within the past 5 years.
– Experience developing novel statistical methods for analyzing large-scale datasets is required.
– Excellent communication skills in both spoken and written English are required.
– Excellent critical thinking and problem-solving abilities are required.
– U.S. permanent residency status or ability to obtain visa is required.
– Experience with discrete Bayesian modeling (e.g. topic modeling) is preferred.