Education

The CICS provides a distinct integration of the mathematical sciences with the physical, chemical, and biological sciences, which supports significant opportunities for graduate education. In collaboration with the College of Engineering and College of Science, the Center is exploring the integration of interdisciplinary courses, such as: 

  • Uncertainty Quantification
  • Stochastic Multiscale Modeling 
  • Inverse Problems
  • Model Reduction
  • Data Assimilation
  • Design Under Uncertainty and Stochastic Optimization
  • Rare Events Modeling

New application-oriented curricula that emphasizes the integration of data- and information-sciences with computational science is also being pursued.

Ultimately, the interdisciplinary educational opportunities led by the CICS aim to bridge the gap at better understanding the role of uncertainty in science and engineering practices. The critical nature of predictive modeling, paired with the CICS's unified curriculum, supports professional growth that can place students as leaders in both industry and academia. 

To learn more about forthcoming masters and PhD opportunities, please contact Nicholas Zabaras.