Scientific Computing and Applied Mathematics
Why Scientific Computing and Applied Mathematics?
The Department of Applied Mathematics offers an M.S. program in scientific computing and applied mathematics (SciCAM). SciCAM students learn a combination of cutting-edge computational methods for modern high-performance computing architectures and applied mathematical modeling. The program is designed to meet the growing demand for graduates in the expanding fields of computational mathematics, modeling, and high-performance computing. Professionals with these skills are highly sought after by industry and government agencies.
The program offers both one year (accelerated) and two year (standard) tracks, providing flexibility to meet students’ needs. With a wide range of elective courses, students can specialize in various areas, including research and coursework tracks. The program emphasizes a hands-on approach to scientific computing and applied mathematics, focusing on practical, real-world applications of mathematical modeling. Small class sizes ensure an excellent student-to-faculty ratio, fostering close interaction. Students also have the potential for internships in Silicon Valley, and scholarships are available to help support their education. For more information about our graduate programs, contact the graduate advisor.
4+1 Pathway to SciCAM
Undergraduate students currently enrolled in approved programs have the opportunity—any time between the start of their junior year and the end of the fall quarter of their senior year—to join the 4+1 Contiguous Pathway leading to the SciCAM degree program. Qualified undergraduates from other undergraduate majors may also apply to this pathway, and their applications will be considered on a case by case basis.
You can find more information about the SciCAM Contiguous Pathway on the UCSC Catalog or on the 4+1 Information page. To apply to the pathway, please schedule an appointment with the SciCAM (AM) 4+1 Coordinator.
Meet your SciCAM advising team
Graduate Director
Program Learning Outcomes
Scientific Computing and Applied Mathematics
- The ability to take a real-life science or engineering problem, and create a mathematical model of it, under supervision or with the help of discussions with colleagues.
- Proficiency in analytical methods for the solution of linear algebra problems, ordinary and partial differential equations.
- Proficiency in the construction of numerical algorithms for the solution of linear algebra problems, as well as ordinary and partial differential equations.
- Proficiency in at least two scientific computing languages such as Fortran, C, Python, R, Matlab, etc. Familiarity with Unix-type operating systems, the use of compilers, professional scientific computing libraries, efficient I/O algorithms, data visualization tools, etc.
- Proficiency in the two main parallel computing paradigms (shared vs. distributed memory) and in the use of OpenMP and MPI. Familiarity with parallel architectures and with supercomputing environments such as batch submission scripts, data transfer protocols, scripting, etc.
- The ability to identify and implement, among all of the existing methods and languages, the most appropriate and efficient approach for the problem posed.
- The ability to analyze the results critically from the model obtained, and to present them to peers in a clear and coherent way in a form of scientific writing and oral presentation.