Computational biologists apply theoretical principles and develop analysis and inference methods to decipher and understand biological systems. They are experts in quantitative approaches, data science and algorithms, while also having in-depth knowledge of complex biological processes. Particularly in cancer biology, there is a growing need for computational biology leaders with expertise and understanding in both quantitative and biological sciences.
Exceptional quantitative scientists experience a strong pull from the technology industry, and may not have a background in biology, so special attention is needed to recruit and train these scientists in biological research. One way to address this area is by creating a new funding mechanism with a specific emphasis on data science (rather than simply including data scientists as part of an existing award program).
This award program is designed to encourage quantitative scientists (trained in fields such as mathematics, computer science, physics, engineering, or related) to pursue research careers in computational biology under the joint mentorship of leaders in both computational science (“dry lab”) and cancer biology (“wet lab”). By investing in this area, Damon Runyon will bring additional attention to the importance of these specially trained scientists for making meaningful progress in cancer biology.
|Year of Award
Open to quantitative scientists with an interest in the intersection between computational biology, data science and cancer research. Applicants must have completed one or more of the following degrees or its equivalent: MD, PhD, MD/PhD, DDS, DVM, DO. The applicant must include a copy of their diploma to confirm date of conferral. (If an applicant has not yet received their PhD diploma but has successfully completed all PhD requirements, including PhD defense, they may submit a letter from the graduate school explicitly stating such, with the date of the successful PhD defense and date of degree conferral.) Applicants require two committed Mentors, one from the field of computational science (“dry lab”) and one from the area of cancer biology (“wet lab”).