Scientific Computing, MSE
The MSE in Scientific Computing (SCMP) program at Penn provides multifaceted education in the fundamentals and applications of computational science. This education program provides a rigorous computational foundation for applications to a broad range of scientific disciplines. An education in SCMP combines a comprehensive set of core courses centered on numerical methods, algorithm development for high performance computational platforms, and the analysis of large data, and offers flexibility to specialize in different computational science application areas. Students may elect to pursue a thesis in computationally-oriented research within the School of Engineering and Applied Science.
We welcome applications from candidates who have a strong background in physical or theoretical sciences, engineering, math, or computer science. Some experience with computer programming is also strongly recommended.
For more information: https://pics.upenn.edu/masters-science-engineering-scientific-computing/
Curriculum
10 course units are required for the MSE in Scientific Computing.
| Code | Title | Course Units |
|---|---|---|
| Foundations | ||
| CIT 5900 | Programming Languages and Techniques | 1 |
| or CIT 5910 | Introduction to Software Development | |
| CIT 5960 | Algorithms and Computation | 1 |
| Core Requirements | ||
| ENM 5020 | Numerical Methods and Modeling | 1 |
| CIS 5450 | Big Data Analytics | 1 |
| Select 1 of the following: | 1 | |
| Applied Machine Learning | ||
or CIS 5200 | Machine Learning | |
or STAT 5710 | Modern Data Mining | |
| Methods and Applications Electives | ||
| Select 2 Methods for Natural Science/Engineering courses | 2 | |
| Select Thesis/Independent Study or 2 Applications/Engineering electives | 2 | |
| Select 1 free elective. Any course in math, science and/or engineering. Subject to advisor approval | 1 | |
| Total Course Units | 10 | |
Technical & Depth Area Electives
| Code | Title | Course Units |
|---|---|---|
| Applications - Any graduate course which focuses on applications in natural science and engineering. Subject to advisor approval | ||
| Thesis/Independent Study 1 | ||
| Methods | ||
| Machine Perception | ||
| Computer Vision & Computational Photography | ||
| Atomic Modeling in Materials Science | ||
| Simulation Modeling and Analysis | ||
| Multiscale Modeling of Chemical and Biological Systems | ||
| Molecular Modeling and Simulations | ||
| Computational Science of Energy and Chemical Transformations | ||
| Finite Element Analysis | ||
| Computational Mechanics | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Biomedical Image Analysis | ||
| Modelling | ||
| Biomedical Image Analysis | ||
| Introduction to Bioinformatics | ||
| Topics In Computational Science and Engineering | ||
| Introduction to Bioinformatics | ||
MTR 5350 | ||
| Fundamentals of Computational Biology | ||
| Fundamentals of Computational Biology | ||
| Fundamentals of Computational Biology | ||
| Biomedical Image Analysis | ||
| Biomedical Image Analysis | ||
| Priniples of Deep Learning | ||
- 1
Select 2 course units of SCMP 5970 Thesis Research or SCMP 5990 Independent Study.
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2022 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.