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/.
- 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.
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.
Job Location: Boston, MA
Tracking Code: 2205
Salary Grade: Competitive
Position Type: Full-Time/Regular