Innovation Lab: Advancing Cancer Biology at the Frontiers of Machine Learning and Mechanistic Models

The National Cancer Institute and Carnegie Mellon University are hosting an Innovation Lab to explore opportunities to combine advances in Artificial Intelligence with the mechanistic modeling approaches of cancer systems biology on June 1-5, 2020. Independent investigators with an interest in combining data driven and mechanistic modeling approaches to cancer biology and expertise in fields such as data science, mathematics, computer science, systems biology, cancer biology, translational research etc. are strongly encouraged to apply. Investigators with access to cancer-related datasets and/or well-defined cancer-relevant questions will be especially poised to benefit from the Innovation Lab.

 

The goal of the Innovation Lab: Advancing Cancer Biology at the Frontiers of Machine Learning and Mechanistic Models is to bring together experts from a wide variety of fields to form new collaborations, ideate and refine new projects, and identify opportunities to accelerate research on the application of data driven and mechanistic modeling approaches to compelling cancer questions.  Applicants (25) who demonstrate a collaborative spirit and represent a variety of different disciplines, backgrounds and approaches will be selected to participate.  The deadline to apply is March 20.  Selected participants will have their travel and hotel accommodations covered by the National Cancer Institute.  At the end of the lab, participants will have met and formed new collaborations around innovative ideas with investigators from different fields.  Meritorious pilot projects developed through the lab will also be eligible for seed funding. Applicants must be willing to commit to stay for the entire duration (Monday morning– Friday afternoon) of the Innovation Lab.

 

Please share this information with any colleagues you think may be interested.

For more information please visit https://hub.ki/groups/mechanisticapproachesinnovationlab

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