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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. At the same... Read more 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. 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.Read less
Program overview
Main Subject
Physics and Astronomy
Study Level
PHD
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. At the same... Read more 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. 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.Read less
Admission Requirements
6.5+
Students need to have a minimum score of 550 on paper based TOEFL.
5 Years
Jan-2000
Tuition fees
Domestic
0 USD
International
0 USD
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
PhD - Doctor of Philosophy in Computational Science
San Diego State University, San Diego , United States
60 monthsProgram duration
Physics and AstronomyMain Subject Area
Program overview
Main Subject
Physics and Astronomy
Study Level
PHD
Read more 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. 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. Read less
Program overview
Main Subject
Physics and Astronomy
Study Level
PHD
Read more 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. 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. Read less
Admission Requirements
Tuition fees
Domestic
International
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
Scholarship Applications: Frequently Asked Questions
Get answers to all your questions about scholarship applications, including tips on how to find scholarships and chances of success.
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Find scholarships to study abroad with our lists of international scholarships – categorized by country, by subject, and by type of student.
How to get a full scholarship
Looking for a fully-funded scholarship to see you into university? Find out how to boost your chances of getting one.
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