Computational science and engineering is essential for a variety of disciplines, including national security, biology, weather prediction, and industrial design.
Ph.D.-trained engineers with skills and credentials in high performance computing are highly sought after and can expect exciting and rewarding career opportunities.
The Graduate Minor is designed to allow doctoral students doing work that involves high-performance computing to add a credential to their degree.
The minor is open to all Ph.D. students in Notre Dame’s College of Engineering and College of Science.
The Minor requires 12 credit hours of course work in computations and related areas plus a dissertation involving high-performance computing. Two of the courses must be beyond the minimum course requirement for the student’s regular program.
The courses must be selected from the list below, or be approved by the coordinator of the minor. At least one course must be from the Numerical Methods group and one course must be from the Scientific Computing group. Many of the courses are taught every year, thus providing ample opportunities to complete the minor during a doctoral study.
- AME 60541: Finite Element Methods
- AME 60613: Finite Elements in Engineering
- AME 60614: Numerical Methods
- AME 60714: Advanced Numerical Methods
- AME 60741: Computational Nonlinear Solid Mechanics
- AME 70732: Computational Fluid Dynamics
- AME 90936: Computational Fluid Dynamics
- CBE 60499: Nonlinear and Stochastic Optimization
- CBE 60547: Computational Chemistry
- CE60130: Finite Elements in Engineering
- ACMS 60395: Numerical Linear Algebra
- ACMS 60690: Numerical Analysis I
- PHYS 50051/ACMS 50051: Numerical PDE Techniques for Scientists and Engineers
- PHYS 60050: Computational Physics
Scientific (HP) Computing
- ACMS 60212: Advanced Scientific Computing
- CSE 60755: Parallel Computing
- CSE 60771: Distributed Systems
Data and Statistics
- ACMS 60801: Statistical Interference
- ACMS 60850: Applied Probability
- ACMS 60852: Statistical Methods in Data Mining
- ACMS 60885: Applied Bayesian Statistics
- ACMS 70780: Categorical Discrete Data
- ACMS 70860: Stochastic Analysis
- AME 60617: Bayesian Data Assimilation and Parameter-State Estimation in Scientific Computing
- AME 70779: Statistical Computing Methods for Scientists and Engineers
- AME 70790: Bayesian Methods for Surrogate Modeling and Dimensionality Reduction
- CSE 60625: Advanced Topics in Machine Learning
- CSE 60627: Machine Learning
- CSE 60647: Data Mining
- CSE 60884: Complex Networks
- CE 60140/40140: Applied/Computational probability
- PHYS 60070: Computing and Data Analysis for Physicists
To apply, you must have completed the oral candidacy exam and dissertation proposal per the requirements of your home department. Complete the application form and get signatures from the Coordinator of the Graduate Minor, Prof. Karel Matous.
Once your dissertation is complete, complete the course approval form and get signatures from the Director of Graduate Studies of your home department and the Coordinator of the Graduate Minor, Prof. Karel Matous.
Completed forms should be sent to Carly Reynolds at firstname.lastname@example.org.