PRINCIPAL INVESTIGATORS/MENTORS:

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.

PROJECT DESCRIPTION:

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.

Required Skills

• 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.

PLEASE EMAIL CV/COVER LETTER TO JOSHUA CAMPBELL, PH.D, ASSISTANT PROFESSOR (camp@bu.edu) DIVISION OF COMPUTATIONAL BIOMEDICINE, DEPARTMENT OF MEDICINE

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. We are a VEVRAA Federal Contractor.

Required Experience

• 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.

PLEASE EMAIL CV/COVER LETTER TO JOSHUA CAMPBELL, PH.D, ASSISTANT PROFESSOR (camp@bu.edu) DIVISION OF COMPUTATIONAL BIOMEDICINE, DEPARTMENT OF MEDICINE

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. We are a VEVRAA Federal Contractor.