Postdoctoral Fellow in Computational Genomics and Cancer Data Science

Pay Range : $55,439 100,940 Company Overview :

The people of Memorial Sloan Kettering Cancer Center (MSK) are united by a singular mission : ending cancer for life. Our specialized care teams provide personalized, compassionate, expert care to patients of all ages.

Informed by basic research done at our Sloan Kettering Institute, scientists across MSK collaborate to conduct innovative translational and clinical research that is driving a revolution in our understanding of cancer as a disease and improving the ability to prevent, diagnose, and treat it.

MSK is dedicated to training the next generation of scientists and clinicians, who go on to pursuer mission at MSK and around the globe.

One of the world’s most respected comprehensive centers devoted exclusively to cancer, we have been recognized as one of the top two cancer hospitals in the country by U.

S. News & World Report for more than 30 years.

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Job Description :

Exciting Opportunity at MSK : Postdoctoral Fellow in Computational Genomics and Cancer Data Science

We are seeking computational postdoctoral researchers with a strong background in machine learning and an interest in developing and applying quantitative methods to problems in translational cancer biology and cancer data science.

A successful candidate will develop and apply deep learning and artificial intelligence methods to analyze large scale datasets.

We are particularly interested in the area of multimodal integration of real-world data from patients treated at our institution, including clinical, genomic, pathology and other data modalities.

Previous experience working with transformers and foundation models is a strong asset. The candidate will have access to state-of-the-art computational infrastructure and unrivaled data resources, including a unique cohort of >

100,000 tumors with available genomic sequencing and co-registered digital pathology slides.

Representative publications from our group include :

• Sanchez-Vega, F. et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell (2018).
• Nguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell (2022).
• Boehm, K. et al. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer (2021).
• Vanguri RS et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer . (2022).
• Boehm, K. M. et al. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer (2022).
• Boehm, K. M. et al. Multimodal histopathologic models stratify hormone receptor-positive early breast cancer. bioRxiv (2024).

Role Overview :

• Carry out research in the field of artificial intelligence applied to computational oncology.
• Dive deeply into multimodal datasets and ask novel and clinically relevant questions for translational cancer research.
• Work together with clinical and biological scientists to advance and execute hypothesis driven research.
• Prepare and contribute to scientific manuscripts for publication in journals and conferences.
• Collaborate with academic partners at other institutions and industry partners in biotech, health data, and big tech.
• Engage with a vibrant community of researchers by presenting at internal work-in-progress meetings and external scientific conferences.

Core Skills :

• A science-focused individual with a passion for advancing computational and / or domain specific research questions
• Someone with a strong quantitative background, great programming skills and a genuine interest in cancer research.
• Excited by new datasets and the opportunity to look at data in new ways.
• A strong communicator and collaborator with a team-oriented mindset required to effectively collaborate with clinicians, biologists and engineers.

Key qualifications :

• A Ph.D. in Applied Mathematics, Machine Learning, Computational Biology or Computer Science with emphasis on applications in molecular biology and / or computational pathology (required).
• Strong programming skills (Python and / or R) and experience developing and training state-of-the-art computer vision models, such as vision transformers, using PyTorch for whole-slide image analysis (preferred).
• Research experience in statistics and computational genomics, and familiarity with next-generation sequencing data and analysis tools (preferred).
• Excellent oral and written English communication skills; experience in delivering oral scientific presentations and writing scientific articles.

Contact :

If you have any questions about this opportunity, please contact Dr. Francisco Sanchez-Vega ([email protected]).

Pay range : $55,439 100,940

Compensation is based on multiple variables. This range represents annual salary only and does not include supplemental performance-based pay or any one-time payments that eligible candidates may be offered at the time of hire.

Salary will be commensurate with experience and the cost of living in New York City. Subsidized housing close to the MSK campus is available.

Helpful links :

MSK's Compensation Philosophy

Closing :

MSK is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment.

All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, disability, veteran status, or any other factor which cannot lawfully be used as a basis for an employment decision.

Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job.

Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.