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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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