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Doctoral Programs
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 Ph.D's in science and engineering
eventually find permanent employment in
academia. Even less frequently do science
Ph.D'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 Ph.D'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 Ph.D. 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
Ph.D'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 Ph.D. 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.
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