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.

1. All Methods and Simulations courses count as Applications in Natural Science courses.

2. All Applications in Natural Science courses count as Free Elective courses.

3. Applications in Natural Science courses cannot count toward Methods and Simulations.

4. Students cannot use Machine Learning courses to count toward the Methods and Simulations requirements.

Core Requirements2
Numerical Methods and Modeling
Big Data Analytics
Computational Mathematics1
Select one of the following:
Numerical and Applied Analysis I
Mathematical Modeling in Physiology and Cell Biology
Numerical Methods for PDEs
Fundamentals of Linear Algebra and Optimization
Machine Learning2
Select two of the following:
Applied Machine Learning
Machine Learning
Modern Data Mining
Data-driven Modeling and Probabilistic Scientific Computing
Data Mining: Learning from Massive Datasets
Applications in Natural Science2
Select two of the following:
Brain-Computer Interfaces
Advanced Methods and Health Applications in Machine Learning
Principles of Genome Engineering
Engineering Biotechnology
GPU Programming and Architecture
Theory of Machine Learning
Interactive Computer Graphics
Advanced Topics in Machine Perception
Fundamentals of Materials
Materials and Manufacturing for Mechanical Design
Transport Processes I
Electrochemistry for Energy, Nanofabrication and Sensing
Mechanical Properties of Macro/Nanoscale Materials
Electronic Properties of Materials
Statistical Mechanics
Introduction to Robotics
Advanced Robotics
Systems Biology of Cell Signaling Behavior
Physics of Medical / Molecular Imaging
Learning in Robotics
Failure Analysis of Engineering Materials
Molecular Biology and Genetics
Advanced Fluid Mechanics
Quantum Circuits and Systems
Quantum Engineering
Special Topics
OR 2 C.U. Master's Thesis/Independent Study
Master's Independent Study
Master's Thesis Research
Methods and Simulations2
Select two of the following:
Theoretical and Computational Neuroscience
Multiscale Modeling of Chemical and Biological Systems
Biomedical Image Analysis
Molecular Modeling and Simulations
Computational Science of Energy and Chemical Transformations
Introduction to Bioinformatics
Fundamentals of Computational Biology
Machine Perception
Computer Vision & Computational Photography
Numerical Methods for PDEs
Machine Learning and Its Applications in Materials Science
Introduction to High-Performance Scientific Computing
Modern Convex Optimization
Simulation Modeling and Analysis
Finite Element Analysis
Computational Mechanics
Atomic Modeling in Materials Science
Principles of Deep Learning
Analysis of Algorithms
Advanced Computer Graphics
Computer Animation
Free Elective1
Please speak with one of the advisors for free elective approval.
Total Course Units10

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.