A variety of exposures or other biological processes can cause mutations in DNA. Many mutational signatures have been identified across different tumor types. These signatures can provide a record of environmental exposure and can give clues about the causes of disease. We are developing statistical models to characterize novel aspects of mutational signatures and improve the speed and accuracy of these tools in large-scale datasets using cutting-edge inference procedures. Successful development of such models will aid researchers in the quest to understand better that factors that produce mutations in human cancers.
At Boston University (BU), we are seeking a postdoctoral candidate with expertise in developing and implementing novel statistical models such as those commonly used in topic modeling. Candidates with experience developing other types of probabilistic models will also be considered. This project is funded by a U01 from the Informatics Technologies for Cancer Research (ITCR) program from the National Cancer Institute (NCI) and led by Drs. Joshua Campbell, Masanao Yajima, and Jonathan Huggins. While funding for this position is available through 2024, independent fellowships will also be encouraged and supported.
Joshua D. Campbell is an Assistant Professor in the Division of Computational Biomedicine at Boston University School of Medicine. The focus of the Campbell lab is to develop and apply computational methods for high-throughput genomic technologies to understand and characterize various biological systems and diseases, including cancer initiation and progression, the mutational burden of carcinogens, the response to cigarette smoke, and lung development. More information can be found at camplab.net.
Dr. Masanao Yajima is a Professor of the Practice in the Department of Mathematics & Statistics. He has developed methods and tools for analyzing biomedical and bioinformatics research and social science. He is the director of the MSSP Statistical Consulting unit and runs the MS in Statistical Practice (MSSP). He has supervised successful interdisciplinary consulting and collaborative projects in various fields, including bioinformatics, biology, epidemiology, marketing, psychology, etc.
Dr. Jonathan Huggins is an Assistant Professor in the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences. His group’s research centers on the development of fast, trustworthy machine learning and AI methods that balance the need for computational efficiency and the desire for statistical optimality with the inherent imperfections that come from real-world problems, large datasets, and complex models.
– Ph.D. or equivalent in biostatistics, statistics, computer science, computational biology, or a related field within the past five years.
– Excellent communication skills in both spoken and written English
– Excellent independent critical thinking and problem-solving abilities are required.
– Experience developing novel statistical algorithms and using Bayesian inference methods (MCMC, variational inference, etc.)
– Experience with statistical programming languages such as Stan, PyMC3, or Pyro (PyTorch) is preferred.
Interested individuals should email a cover letter and CV to email@example.com. Boston University is an equal opportunity employer and gives consideration for employment to qualified applicants without regard to race, color, religion, sex, age, national origin, physical or mental disability, sexual orientation, gender identity, genetic information, military service, or veteran status or any other characteristic protected by law.