Tenure-Track Leader in Cancer Systems & Computational Biology

Albert Einstein College of Medicine (Bronx, NY); New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Albert Einstein College of Medicine in New York invites applications for a tenure-track faculty position in the Department of Systems and Computational Biology in collaboration with the Montefiore Einstein Comprehensive Cancer Center.

The ideal candidate will lead innovative, quantitative cancer biology research across computational, mathematical, and engineering disciplines, collaborating with clinical and experimental investigators.

Qualifications

• Demonstrated excellence in a quantitative discipline such as computational biology, systems biology, computer science, statistics, mathematics, physics, or engineering.
• Strong record of innovation in computational methods, including machine learning, AI, image analysis, multimodal integration, and/or software development.
• Ability to work collaboratively across disciplines, including with cancer biologists, clinicians, pathologists, epidemiologists, and data scientists.
• Have a clear and compelling research program with the potential to attract external funding and advance the mission of the Department and the Montefiore Einstein Comprehensive Cancer Center.

Responsibilities

• Develop and lead a rigorous research program addressing cancer biology using computational and quantitative approaches.
• Collaborate with experimental and clinical investigators to translate discoveries into patient benefit.
• Contribute to cross-disciplinary training, mentorship, and grant funding initiatives.

Skills

Quantitative discipline

Computational methods

AI/ML

Interdisciplinary collaboration

Job description

Albert Einstein College of Medicine in New York invites applications for a tenure-track faculty position in the Department of Systems and Computational Biology in collaboration with the Montefiore Einstein Comprehensive Cancer Center.

The ideal candidate will lead innovative, quantitative cancer biology research across computational, mathematical, and engineering disciplines, collaborating with clinical and experimental investigators.