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Postdoctoral Research Fellow in Cancer Data Science
2 months ago
Memorial Sloan Kettering Cancer Center (MSK) is seeking a highly motivated Postdoctoral Fellow to join our Computational Oncology program. As a key member of our team, you will contribute to the development of novel data science and machine learning methods that address pressing needs in cancer research.
Key Responsibilities:
- Drive key research questions that crosscut the themes of bayesian statistics, graphical models, causal inference, active learning, spatial modeling, and deep generative models.
- Execute multiple projects that will involve analysis of complex biological and medical data, including multi-omics, ex vivo drug screens, patient treatment histories, and spatial profiling of cancer patient tissues.
- Develop novel computational methods that power ambitious collaborative projects with high translational impact.
Requirements:
- Hold a PhD or will soon acquire one.
- Demonstrated track record in the fields of statistics, machine learning, computational biology, data science, or related fields (computer science, physics, biostatistics, bioinformatics).
- No advanced knowledge of biology or medicine is required for applicants with quantitative backgrounds.
What We Offer:
- Competitive salary and benefits.
- Opportunity to work with a dynamic and highly collaborative team.
- Access to state-of-the-art computational resources and facilities.
How to Apply:
- Include a CV, cover letter, career statement, and contact information for two references in your application submission.
- If you have any questions about this job opportunity, please contact Dr. Wesley Tansey, Principal Investigator.
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.