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.


10 course units are required for the MSE in Scientific Computing.

CIT 5900Programming Languages and Techniques1
or CIT 5910 Introduction to Software Development
CIT 5960Algorithms and Computation1
Core Requirements
ENM 5020Numerical Methods and Modeling1
CIS 5450Big Data Analytics1
Select 1 of the following:1
Applied Machine Learning
Machine Learning
Modern Data Mining
Methods and Applications Electives
Select 2 Methods for Natural Science/Engineering courses2
Select Thesis/Independent Study or 2 Applications/Engineering electives2
Select 1 free elective. Any course in math, science and/or engineering. Subject to advisor approval1
Total Course Units10

Technical & Depth Area Electives

Applications - Any graduate course which focuses on applications in natural science and engineering. Subject to advisor approval
Thesis/Independent Study 1
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
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

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.