By and large, doctoral programs in the sciences are structured to train primarily future academic professionals. However, it is well-documented that only about a third of all PhD's in science and engineering eventually find permanent employment in academia. Even less frequently do science PhD's find an occupation related to the particular subject of their graduate training, or even in the same discipline or general area of science. At the same time, however, the average employment rate for science and engineering PhD's has been consistently above the national average, indicating that such graduates can successfully seek employment outside their immediate field of expertise.
This is doubtless due to their general aptitude for tackling complex tasks, which is in turn a direct outcome of their training in research. Examples abound -- to cite only one, the recent wave of PhD physicists who found employment in the financial industry.
Concurrently, the last decade has witnessed profound changes and restructuring of traditional industrial research and development. Many industrial and corporate laboratories, that had been at one time heavily engaged in cutting edge basic research, were refocused to have a greater and more immediate impact on production, allowing firms to compete on several fronts at the same time, make more efficient use of internal resources, and keep up with rapidly changing technology. This process has naturally favored skilled but broadly educated scientists, who are capable of working beyond the traditional boundary of their own field, typically within multidisciplinary teams.
These qualities are at odds with the narrow focus that characterizes graduate student research in science doctoral programs across the nation. Graduate students are under pressure to produce individual original contributions within a limited field. They are practically never encouraged to explore the possible relevance of what they are learning to other areas of research, or even to familiarize themselves with research themes or terminology from other disciplines. These issues are particularly important in Computational Science, as the great generality of its methods and techniques makes them relevant to virtually any research work. Yet, very seldom are PhD's in scientific disciplines or engineering, even those who have performed computational work for their thesis project, capable of quickly exporting methods and applications to other fields without substantial retraining. The PhD programs in Computational Science and Computational Science with concentration in Statistics provide an alternative to these traditional approaches, accomplishing its goal by pursuing an interdisciplinary approach to graduate training.
- Checklist & Deadlines
- PhD in Computational Science curriculum
- PhD in Computational Science with concentration in Statistics curriculum
- PhD in Computational Science Computational Science Program Handbook
- PhD in Computational Science Computational Science with concentration in Statistics Program Handbook
- Qualifying Exam - Proposal - Dissertation Defense Guidelines