Opportunities

Post-doctoral Associate

Posted October 21, 2019

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.

Requirements:

– 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.

Post-doctoral Associate

Posted July 16, 2019

We are seeking a post-doctoral fellow to work on a collaborative project between the Beane and Kolachalama labs.  The focus of the Beane lab is to develop and implement computational and statistical methodologies to analyze high-throughput genomic data to characterize the effects of tobacco smoke and their contribution to the pathogenesis of two smoking-related lung diseases. The focus of the Kolachalama lab is to develop machine learning and image processing techniques for pattern recognition and understanding pathophysiological mechanisms.  We are currently using genomic methods to understand the mechanisms underlying the development and progression of lung premalignant lesions via the “Pre-Cancer Genome Atlas” consortium funded by Stand Up 2 Cancer (SU2C) and the NCI Pre-Cancer Atlas Initiative.

The lab is seeking a post-doctoral fellow with a strong computational background and programming skills to work on a collaborative project with other faculty members in the section of Computational Biomedicine at Boston University School of Medicine.   The project involves developing deep learning algorithms to automate the pathological assessment of premalignant lesions using whole slide images.  The features can subsequently be correlated with multi-omic data being generated by the large consortia above.  The collaborative project will provide experience with a diverse number of faculty with expertise in genomics, machine learning, and image processing techniques as well as lung biology and pathology.

The position is for 1 year with possible extension.  The candidate 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. Independent fellowships will also be encouraged. Further information can be found at: www.bumc.bu.edu/compbiomed/.

Requirements:

– Ph.D. or equivalent degree in computational biology or computer science or a related field.
– Excellent communication skills in both spoken and written English are required.
– Excellent critical thinking and problem-solving abilities are required.
– Previous experience in using Python for machine learning would be preferable but not mandatory.

All resumes and cover letters should be directed to Jennifer Beane at: jbeane@bu.edu