Postdoctoral Research Fellow - Jeselsohn Lab

Postdoctoral Research Fellow - Jeselsohn Lab 

Dana-Farber Cancer Institute 

Boston, Massachusetts 

Full Time 

The Jeselsohn Lab and the Breast Oncology Center Computational Biology (BCCB) group is looking for a highly motivated and talented postdoctoral researcher with a computational or bioinformatic background to join the Department of Breast Oncology at Dana-Farber Cancer Institute. The candidate for this position will join a team of computational biologists to work on multi-omic sequencing datasets (DNA, RNA, epigenetic, spatial transcriptomics and spatial proteomics) in the context of pre-clinical and multiple clinically oriented studies in order to help advance efforts for translational cancer genomics and precision cancer medicine. The candidate will report to the BCCB Director Dr. Rinath Jeselsohn and the BCCB leadership (Dr. Gomez Tejeda Zanudo, Dr. Daniel Abravanel).

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

OVERVIEW

The Breast Oncology Center Computational Biology group leads the computational biology efforts to characterize multi-omics sequencing datasets that describe the genetic changes that occur across breast cancer. This characterization is done by applying existing and novel computational biology, bioinformatic, and machine learning algorithms to sequencing datasets and correlating them with multi-dimensional clinical datasets that contain treatment and diagnostic information. The aim of these efforts is to identify genomic alterations and transcriptional changes associated with tumor evolutionary dynamics, mechanisms of resistance, and therapeutic benefit. These datasets and multi-omics associations could help identify novel approaches for personalized care in oncology. These datasets and association may provide support for new methods in clinical decision-making, tumor markers for rational drug development, and new insights into tumor biology through innovative analyses. In addition, the Jeselsohn lab utilizes pre-clinical model systems and high-throughput methodologies to investigate mechanisms of drug resistance and novel therapeutic targets in breast cancer.

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 presents at scientific conferences, as well as internal and external project-team reports.
• Actively participate in project team and group meetings.
• Participating within a team of scientists to foster a culture of scientific excellence.
• Assisting 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.

At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are committed to having faculty and staff who offer multifaceted experiences. Cancer knows no boundaries and when it comes to hiring the most dedicated and compassionate professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.  

Pay Transparency Statement

The hiring range is based on market pay structures, with individual salaries determined by factors such as business needs, market conditions, internal equity, and based on the candidate’s relevant experience, skills and qualifications.

For union positions, the pay range is determined by the Collective Bargaining Agreement (CBA).

$72,000.00 - $76,385.00

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