As a component of the faculty cluster initiative, the Chancellor’s Faculty Excellence Program, NC State University is recruiting one tenure track Assistant Professor in mathematical modeling/machine learning to expand the interdisciplinary faculty cluster on Translational Predictive Biology (CTPB). The selected faculty is expected to synergize with existing Translational Predictive Biology Cluster faculty to build an internationally recognized center focused on developing integrated systems and analytical methods that can predict in vivo responses from in vitro systems.

Together with NC State’s existing strength in regenerative medicine and data sciences, this expanded cluster will propel NC State to the forefront of efforts to define new principles in the development and application of data analytics and mathematical modeling to predict clinically relevant biological outcomes using in vitro engineered tissue systems and in vivo models and will play a central role in the development of the Center for Translational Predictive Biology.

We are interested in individuals whose expertise is in the computational realm but who can bridge experiment and theory and have the ability to translate research outcomes to address biomedical challenges. Demonstrated collaboration as a member of an interdisciplinary team is essential. Successful candidates will engage in transformative research that integrates diverse data inputs from increasingly complex in vitro and in vivo systems and utilizes mathematical or statistical modeling and/or data analytics to develop practical, accurate predictive systems. The cluster seeks an individual with expertise in developing innovative approaches to improve:

Hiring is at the level of Assistant Professor. The home department is open and will be determined with the applicant’s input and their credentials and research fit. Hires will be expected to provide key leadership in data science and its application in predictive biology, teach existing courses, and develop specialized courses in their area of expertise.