Quantitative Cancer Research Fellow in AI & Theory

Memorial Sloan Kettering Cancer Center; Street (MD); On-site; USD 77,591 - 85,000; Full time

Job summary

Memorial Sloan Kettering Cancer Center in New York invites a postdoctoral fellow to apply theoretical training to cancer biology. You will develop quantitative models using AI methods and collaborate with experimentalists and clinician-scientists to translate insights into disease understanding.

The role emphasizes independence, weekly chalk talks, and publishing high-impact results while contributing to translational research in a multidisciplinary team.

Qualifications

• PhD in physics, mathematics, computer science, or related quantitative field with demonstrated theoretical research output.
• Strong expertise in frontier modeling concepts and quantitative methods.
• Experience in programming and analysis of biological data is advantageous.
• Track record of exceptional publications in relevant fields.
• Ability to collaborate across disciplines and communicate complex ideas clearly.

Responsibilities

• Apply theoretical training to cancer-related problems and data.
• Develop predictive quantitative models using frontier methods and AI techniques.
• Collaborate with experimentalists and physician-scientists to translate insights into practice.
• Participate in weekly chalk talks and interact with aligned investigators.
• Independently manage research projects and contribute to high-impact publications.

Skills

Frontier modeling

Programming experience

Publications track record

Interdisciplinary collaboration

Quantitative reasoning

Education

PhD in physics, mathematics, computer science, or related quantitative field

Job description

Memorial Sloan Kettering Cancer Center in New York invites a postdoctoral fellow to apply theoretical training to cancer biology. You will develop quantitative models using AI methods and collaborate with experimentalists and clinician-scientists to translate insights into disease understanding.

The role emphasizes independence, weekly chalk talks, and publishing high-impact results while contributing to translational research in a multidisciplinary team.