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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 pursue our 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.

In compliance with applicable State regulatory authorities, vaccination for COVID-19 and Influenza (flu) is mandatory for all MSK staff who do not work 100% remotely (including new hires ). Staff are considered fully vaccinated upon completion of a primary vaccination series for COVID-19 Exceptions from the COVID-19 vaccine requirement are permitted for those who request and receive an approved medical, religious, or fully remote exemption. Requests for exemption can take up to two weeks to review. Vaccination or an approved medical exemption is required to start work.

Job Description

The Dey Lab in Computational & Systems Biology program at Memorial Sloan Kettering Cancer Center is seeking postdoctoral fellow in machine learning/artificial intelligence and applied statistics to lead inter-disciplinary collaborative projects in biological sciences supervised by Dr. Dey, in collaboration with Dr. Rahul Mazumder, MIT Sloan School of Management (http://www.mit.edu/~rahulmaz/). The candidate will work to

--Develop and apply state-of-the-art local and global matrix completion approaches to omics data in the context of human diseases.

--Build large scale neural network and graph-embedding models on biomedical and cancer knowledge graphs, with applications to predicting patient risk.

--Work on multimodal diffusion and transformer-based language models to connect different modalities of genomic and genetic data.

We welcome applications from candidates with an exceedingly strong quantitative background in computer science, statistics/biostatistics, computational biology, or other related domains. The applicant would be required to have prior experience working on machine learning, optimization, and AI models. The candidate must be proficient in computing, with expertise in Python and computing on GPUs. Prior background in biological sciences will be a plus, but not required.

The candidate's primary affiliation will be with the Sloan Kettering Institute, MSKCC located on the Upper East Side of New York City. It is part of the vibrant Tri-Institutional Research campus adjacent to Rockefeller University and Weill Cornell Medical College. Sloan Kettering offers competitive postdoc salaries and convenient subsidized housing. The candidate will have regular meetings with Drs Dey and Mazumder and will also be working closely with collaborators from various consortia like ADSP, ENCODE, and IGVF. The candidate will also be working closely with the MSKCC AI/ML team to implement large-scale machine learning models.

To apply, please send a CV, a brief cover letter outlining your interest, and names/contact information for three references to the following email addresses:

deyk@mskcc.org

rahulmaz@mit.edu

Salary: $55,439 - 100,940 (depending on postgraduate experience)

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.

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