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Job Description
–Develop and applystate-of-the-artlocal 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 topredictingpatient 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, withexpertisein Python and computing on GPUs. Prior background in biological sciences will be a plus, but notrequired.
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 campusadjacent toRockefeller 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 andMazumderand 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:
Salary:$55,439 – 100,940(depending on postgraduate experience)
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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, veteranstatus,or any other factor which cannot lawfully be used as a basis for an employment decision.
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