Postdoctoral Research Fellow - Jeselsohn Lab

Overview

The Jeselsohn Lab and the Breast Oncology Center Computational Biology (BCCB) group are seeking a postdoctoral researcher with a computational or bioinformatic background to join the Department of Breast Oncology at Dana-Farber Cancer Institute. The candidate will work on multi-omic sequencing datasets to advance translational cancer genomics and precision cancer medicine.

Responsibilities

• Drive scientific projects by analyzing and interpreting large-scale and multidimensional omics datasets and clinical outcomes to advance translational science with immediate potential clinical application.
• Develop and implement AI and machine learning methodologies for integrative analysis of cancer genomic data, including its implementation into software tools that will be widely distributed and shared with the scientific community.
• Participate in the design and implementation of algorithms to analyze and integrate large data as well as the maintenance of datasets and shared tools.
• Lead and contribute to manuscript preparation, scholarly reports, and present at scientific conferences, as well as internal and external project-team reports.
• Actively participate in project team and group meetings.
• Participate in fostering a culture of scientific excellence within a team of scientists.
• Assist in the mentorship of junior analytical staff members and graduate students.

Qualifications

• A PhD in computational biology, bioinformatics, statistics, computer science (Machine Learning), physics, math, engineering or a related quantitative discipline is required.
• Experience with cancer genomics, computational analysis, algorithm development, statistics and machine learning.
• Experience with AI and machine learning methods applied to cancer genomics.
• Proficiency in at least one modern programming language. Experience with a scientific programming environment, such as python or R, is preferred.
• Strong communication skills.
• Excellent oral, written, interpersonal, and organizational skills and the ability to perform both self-directed and guided research are crucial.
• Demonstrated ability to work as part of a large, interdisciplinary team. Ability to meet deadlines and multitask efficiently is a must.