Computational Science and Engineering (CSE) option
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Computational Science and Engineering (CSE) is now an integral part of scientific research and engineering design in which large-scale, dynamic simulation and high-performance computing play a central role. Students electing the CSE option become proficient in advanced computing methods as well as in one or more applied disciplines in science, engineering, mathematics or medicine. The CSE option fosters and coordinates interdisciplinary, computationally-based research and education and prepares students to solve complex technological problems. Graduates are highly sought by research and development focused industries, institutions and government laboratories.
Core courses requirements
The core curriculum for the CSE option includes 9 hours of foundational courses in Mathematical/Computational Methods and High Performance Scientific Computing plus enrollment in EAS/CSE seminars. The latter courses are important to maintain cohesion within the group and to encourage exchange of ideas from a variety of perspectives.
Specialization course requirements
A minimum of 18 additional hours of coursework is required for post-baccalaureate students. Course selection is based on the research and career goals of the student, and curricula will vary between students. The coursework must include courses from at least two disciplines. These courses are usually taken in mathematics, physics, engineering, or computer science.
Dissertation research
This work is completed under the guidance of the students' faculty advisor. More information about faculty and research opportunities in the Scientific Computing group can be found here.
A typical curriculum plan for the CSE option is shown below.
Graduate program curriculum outline
Computational Science and Engineering Option
| Major required (core) courses (Total # of courses required = 7) | ||
|---|---|---|
| Course number | Course title | Credit hours |
| EAS 501 | Advanced Mathematical Methods | 3 |
| EAS 502 | Computational Methods | 3 |
| EAS 520 | High Performance Scientific Computing | 3 |
| EAS 621 | Scientific Computational Research Seminar | 3 |
| EAS 622 | Scientific Computational Research Seminar | 3 |
| EAS 600 | Dissertation Proposal Preparation | 3 |
| EAS 601/701 | Doctoral Dissertation Research | 27 |
| EAS 602 | Research Ethics | 1 |
| EAS 700 | Doctoral Seminar | 2 |
| Subtotal # Core Credits Required | 48 | |
| Elective course choices (Total courses required = 6) (attach list of choices if needed) | ||
| MTH, PHY, MAR 500-600 | 4 x Major Electives (example list attached) | 12 |
| COE, MTH, MAR 500-600 | 2 x Graduate Electives (Minor) | 6 |
| Subtotal # Elective Credits Required | 18 | |
| Curriculum summary | ||
| Total number of courses required for the degree | 13 | |
| Total credit hours required for degree | 66 | |
Prerequisite, Concentration or Other Requirements:
Ph.D. Qualifying Examination (QE) and Comprehensive Exam: Each student must pass a qualifying exam and a comprehensive exam on research preparedness prior to becoming a doctoral candidate. Due to the interdisciplinary nature of the program, courses from the same discipline cannot be used as both major and minor electives. For example, if any MTH courses are used as major electives, MTH courses cannot be used as minor electives. Similarly, if any PHY courses are used as major electives, PHY courses cannot be used as minor electives.
Sample courses
Major Electives for Computational Science and Engineering Option include any graduate-level course in MTH, PHY, and MAR (if deemed closely related to the student's dissertation research).
Minor Electives can be these and other CoE graduate-level courses. Moreover, they should be sufficiently different from the student's chosen categories of major electives. For example, a student can not use one MTH course as a major elective and another MTH course as a minor elective to fulfill the degree requirements.