Computational Biologist

Dana-Farber Cancer Institute

Boston, MA

Job posting number: #7294692 (Ref:df41984)

Posted: December 4, 2024

Job Description

The Dana-Farber Cancer Institute seeks a Computational Biologist in the Gillani Lab in Computational Pediatric Cancer Research (https://gillanilab.dana-farber.org/). The Computational Biologist will work on the analysis of new and existing genomic datasets to advance efforts in Pediatric Cancer Genomics, with the goal of arriving at new insights around cancer risk, clinical biomarkers, and precision cancer medicine to advance the care of pediatric cancer patients.

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The mission of the Gillani Lab is to advance pediatric, adolescent, and young adult cancer care using cutting-edge computational biology approaches. We strive to contribute to a comprehensive understanding of the germline genetics and tumor genomics of pediatric cancer to inform novel approaches to the management of pediatric cancer. We value great science that matters to patients, caring about each other as people and colleagues, and fostering an environment of mutual respect and collaboration.

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The qualified candidate will carry out data analysis, develop and support data processing and analysis tools, create new algorithms for emerging clinically focused needs with an emphasis on bulk and single cell tumor analysis, maintain datasets and databases, integrate tools and databases into existing high-throughput pipelines, and facilitate the display and the distribution of processed data.

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.

Related projects and responsibilities will include, but are not limited to:

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  • Whole-genome and whole-exome sequencing analysis of normal samples from pediatric cancer patients
  • Whole-genome sequencing and single-cell sequencing analysis of tumor samples
  • Studies of resistance to existing and emerging cancer therapies using patient-derived data
  • Studies of patient samples for tumor behavior and clinical outcomes

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Projects are collaborative efforts between the Dana-Farber Cancer Institute, the Broad Institute, Boston Children’s Hospital, the Harvard Medical School, and multiple other institutions. Projects have the potential to identify novel approaches for personalized care in oncology. In addition, this data may provide support for new methods in clinical decision-making, biomarkers for rational drug development, and new insights into tumor biology through innovative analyses. The Computational Biologist hired for this position will join a collaborative team in this effort and will participate in the design and implementation of algorithms to analyze the data and integrate with other data sets including clinical outcomes data.

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This Computational Biologist will also help with the generation of tools needed for manipulating and preparing data for display; transferring data to external collaborators and data repositories; and will also help maintain, support, and document shared tools, code, and data sets. As the field of clinical computational oncology evolves, this position is likely to present diverse and flexible opportunities - from deeper and more complex software design problems, to becoming more involved in the bioinformatic and analytic aspects of predictive modeling. As such, the Computational Biologist should have a willingness to learn new languages and tools as the field grows, as well as have an interest in cancer biology/ medicine more broadly.

  • A B.S. or M.S. in computer science, biomedical engineering, mathematics, or other related disciplines/ comparable research experience
  • Significant experience in computer programming and computational biological applications (experience in R and/ or Python, as well as cloud computing)
  • A strong background in statistics and biology
  • Experience managing and curating large datasets
  • Excellent oral and written communication skills and the ability to perform both self-directed and guided research
  • Outstanding personal initiative and the ability to work effectively as part of a team
  • Ability to meet deadlines and efficiently multitask
  • Ability to effectively collaborate across a range of individuals from various disciplines, including computer science and clinical medicine
  • Ability to seek out mentorship and assistance as needed, while also being a mentor to other members of the community

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 equally committed to diversifying our faculty and staff. Cancer knows no boundaries and when it comes to hiring the most dedicated and diverse professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

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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.

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More Info

Job posting number:#7294692 (Ref:df41984)
Application Deadline:Open Until Filled
Employer Location:Dana-Farber Cancer Institute
Needham,Massachusetts
United States
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