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Postdoctoral Fellow-Data Science for Cancer Screening Research

Dana-Farber Cancer Institute
United States, Massachusetts, Boston
450 Brookline Avenue (Show on map)
Dec 11, 2023

Job ID:
29456


Location:
450 Brookline Ave,
Boston,
MA 02215


Category:
Fellowships


Employment Type:
Full time


Work Location:
Hybrid: 2-3 days onsite/week



Overview

The Hale Family Center for Pancreatic Cancer Research at Dana-Farber Cancer Institute () is seeking to hire a full-time postdoctoral fellow/data scientist to support our pancreatic cancer research program. This an extraordinary opportunity to develop novel data science techniques to find early signs of pancreatic cancer in a rich dataset of over 1 million patient records. This work will be supported by the world-class resources of Dana-Farber Cancer Institute and Harvard Medical School as part of a $50M gift for the Hale Family Center for Pancreatic Cancer ()This individual will work closely with a multidisciplinary team that is developing new approaches to the early detection and treatment of pancreatic cancer.

This work will be conducted on site at Dana-Farber Cancer Institute.

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.

Responsibilities

This individual will be responsible for conducting and coordinating state-of-the-art data science research within our larger multidisciplinary research team. They will work with a team of masters-level data scientists and will be responsible for ensuring methodologic quality, maintaining scientific rigor, and stimulating scientific innovation. This work will include the design, training, and validation of cancer risk models that incorporate diverse data sources, including medical images, clinical text, and laboratory assay data.

Qualifications

Requirements include a PhD, MD or doctoral equivalent degree in data science, computer science, computational biology, or a related discipline. The successful candidate will have fluency with the design, training, and validation of state-of-the-art artificial intelligence algorithms, including neural networks, and will have published innovative methods in this domain. Candidates must be proficient in programming with python and with common AI training platforms. Candidates should have experience in the analysis of multimodality data. Experience with risk modeling, time series data, natural language processing, and/or medical image analysis will be desirable.

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.

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 groups as protected by law.


Job ID:
29456


Location:
450 Brookline Ave,
Boston,
MA 02215


Category:
Fellowships


Employment Type:
Full time


Work Location:
Hybrid: 2-3 days onsite/week


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