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.
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.
| Code | Title | Course Units |
|---|---|---|
| Core Requirements | 2 | |
| Numerical Methods and Modeling | ||
| Big Data Analytics | ||
| Computational Mathematics | 1 | |
| Select one of the following: | ||
| Numerical and Applied Analysis I | ||
| Mathematical Modeling in Physiology and Cell Biology | ||
| The Mathematics of Medical Imaging and Measurement | ||
| Numerical Methods for PDEs | ||
| Fundamentals of Linear Algebra and Optimization | ||
| Machine Learning | 2 | |
| Select two of the following: | ||
| Applied Machine Learning | ||
| Machine Learning | ||
| Deep Learning for Data Science | ||
| Theory of Machine Learning | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Data Mining: Learning from Massive Datasets | ||
| Principles of Deep Learning | ||
| Learning in Robotics | ||
| Machine Learning and Its Applications in Materials Science | ||
| Modern Data Mining | ||
| Applications in Natural Science | 2 | |
| Select two of the following: | ||
| Brain-Computer Interfaces | ||
| Physics of Medical / Molecular Imaging | ||
| Molecular Biology and Genetics | ||
| Introduction to Computational Biology & Biological Modeling | ||
| Advanced Methods and Health Applications in Machine Learning | ||
| Principles of Genome Engineering | ||
| Engineering Biotechnology | ||
| Advanced Molecular Thermodynamics | ||
| Advanced Chemical Kinetics and Reactor Design | ||
| GPU Programming and Architecture | ||
| Interactive Computer Graphics | ||
| Advanced Topics in Machine Perception | ||
| Quantum Engineering | ||
| Tribology | ||
| Failure Analysis of Engineering Materials | ||
| Fundamentals of Materials | ||
| Materials and Manufacturing for Mechanical Design | ||
| Introduction to Robotics | ||
| Viscous Fluid Flow and Modern Applications | ||
| Turbulence | ||
| Performance, Stability and Control of UAVs | ||
| Aerodynamics | ||
| Transport Processes I | ||
| Electrochemistry for Energy, Nanofabrication and Sensing | ||
| Advanced Robotics | ||
| Advanced Fluid Mechanics | ||
| Mechanical Properties of Macro/Nanoscale Materials | ||
| Electronic Properties of Materials | ||
| Statistical Mechanics | ||
| Transmission Electron Microscopy | ||
| Advanced Synchrotron and Electron Characterization of Materials | ||
| Particle Cosmology | ||
| And any Methods and Simulations courses | ||
| OR 2 C.U. Master's Thesis/Independent Study | ||
| Master's Independent Study | ||
| Master's Thesis Research | ||
| Methods and Simulations | 2 | |
| Select two of the following: | ||
| Introduction to High-Performance Scientific Computing | ||
| 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 | ||
| Advanced Computer Graphics | ||
| Computer Animation | ||
| Machine Perception | ||
| Computer Vision & Computational Photography | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Simulation Modeling and Analysis | ||
| Introduction to Optimization Theory | ||
| Modern Convex Optimization | ||
| Finite Element Analysis | ||
| Computational Mechanics | ||
| Atomic Modeling in Materials Science | ||
| Free Elective | 1 | |
| Please speak with one of the advisors for free elective approval. | ||
| Total Course Units | 10 | |
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.