Learning across the boundaries between engineering, science, technology and business
Ajit D. Kelkar, Narayan Radhakrishnan, Kenneth Murray · 4th ASEE/AaeE Global Colloquium on Engineering Education · 2005
North Carolina Agricultural and Technical State University (NCA and T) has established a master's degree program in Computational Science and Engineering (CSE). The program will be highly interdisciplinary, drawing expertise and resources from various disciplines across the University, and operating outside a department. It will offer an interdisciplinary curriculum combining applied mathematics, high performance parallel and scalable computing, scientific modeling and simulation, data visualization, and domain areas such as physical science and engineering, life sciences, agricultural and environmental sciences, technology and business and will help in learning across the boundaries between engineering, science, technology and business. The newly established MS degree program in CSE builds upon the University's curricular strength and research capability in science, engineering, mathematics, technology, and business. It is a result of interdisciplinary collaboration among the College of Arts and Sciences, College of Engineering, School of Agriculture and Environmental Sciences, School of Business and Economics, and the School of Technology. It will enhance and supplement current graduate research and education programs in science, engineering, mathematics, technology and business, and further the fertilizing and nurturing of cross-disciplinary interaction and collaboration in CSE among faculty and graduate students. As the first stand-alone CSE graduate degree program in the State of North Carolina. This CSE master's program would have three tracks with a focus on computational science, but distinguish across the domain areas of specialization. The three tracks with a common curriculum in their core courses will account for the variations in computational science field requirements across the several domains. The tracks are interdisciplinary in nature, and are primarily based on the variations in the background and training in the computational areas between the undergraduate domains. These are not grouped to conform to the individual colleges/schools these domain areas come under.