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Scientific Computing, MSE

Applications are now closed. Please apply through our online application next year.

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 strongly recommended.

Interested in career prospects following a master’s degree from Penn Engineering? Please check out the attached report from 2022.

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

  1. All Simulations count as Applications, all Applications count as Free Electives (cannot go backwards).
  2. Students cannot use Machine Learning courses to count toward the Simulations requirement. Please consult with your advisor prior to registering for courses each semester.

**If you enrolled in SCMP prior to Fall of 2023, your program of study may look slightly different. Please adhere to the old program of study found here. If you have questions about this, email Julia at

Computational Mathematics (2 C.U.)

ENM 5020 (1 C.U.) and 1 C.U. of the following:

  • AMCS 5681: Mathematical Modeling in Physiology and Cell Biology
  • AMCS 5840: The Mathematics of Medical Imaging and Measurement
  • AMCS 6025: Numerical and Applied Analysis I
  • ENM 5220: Numerical Methods for PDEs
  • CIS 5150: Fundamentals of Linear Algebra and Optimization
Machine Learning and Data Science (3 C.U.)

CIS 5450 (1 C.U.) and 2 C.U. of the following:

  • CIS 5190: Applied Machine Learning
  • CIS 5200: Machine Learning
  • CIS 5210: Artificial Intelligence
  • CIS 5220: Deep Learning for Data Science
  • CIS 6200: Advanced Topics in Machine Learning
  • CIS 6250: Theory of Machine Learning
  • ENM 5310: Data-driven Modeling and Probabilistic Scientific Computing
  • ESE 5450: Data Mining: Learning from Massive Datasets
  • ESE 5460: Principles of Deep Learning
  • ESE 6500: Learning in Robotics
  • MSE 5760: Machine Learning and Its Applications in Materials Science
  • STAT 5710: Modern Data Mining
Methods and Simulations (2 C.U.)

2 C.U. of the following:

Bioengineering (BE)
  • BE 5160: Introduction to High-Performance Scientific Computing
  • BE 5300: Theoretical and Computational Neuroscience
  • BE 5370: Biomedical Image Analysis
Chemical and Biomolecular Engineering (CBE)
  • CBE 5250: Molecular Modeling and Simulations
  • CBE 5440: Computational Science of Energy and Chemical Transformations
Computer and Information Science (CIS)
Engineering Mathematics (ENM)
  • ENM 5310: Data-driven Modeling and Probabilistic Scientific Computing
Electrical and Systems Engineering (ESE)
  • ESE 5030: Simulation Modeling and Analysis
  • ESE 5060: Introduction to Optimization Theory
  • ESE 6050: Modern Convex Optimization
Mechanical Engineering and Applied Mechanics (MEAM)
Materials Science and Engineering (MSE)
  • MSE 5610: Atomic Modeling in Materials Science
Scientific Computing Master’s Program (SCMP)
  • SCMP 5590: Multiscale Modeling of Chemical and Biological Systems
Applications in Natural Science (2 C.U.)

*All simulations courses also count toward the applications requirement.

SCMP 5970: Master’s Thesis Research (2 C.U.)

SCMP 5990: Master’s Independent Study (up to 2 C.U.)**

**If you only take 1 C.U., you will need to take 1 C.U. of one of the below courses to fulfill this requirement.

or 2 C.U. of the following:

Bioengineering (BE)
  • BE 5210: Brain-Computer Interfaces
  • BE 5610: Musculoskeletal Biology and Bioengineering
  • BE 5690: Systems Biology of Cell Signaling Behavior
  • BE 5810: Techniques of Magnetic Resonance Imaging
  • BE 5830: Physics of Medical / Molecular Imaging
Biology (BIOL)
  • BIOL 5210: Molecular Biology and Genetics
  • BIOL 5535: Introduction to Computational Biology & Biological Modeling
Biomedical Informatics (BMIN)
  • BMIN 5210: Advanced Methods and Health Applications in Machine Learning
  • BMIN 5220: Natural Language Processing for Health
Chemical and Biomolecular Engineering (CBE)
  • CBE 5170: Principles of Genome Engineering
  • CBE 5540: Engineering Biotechnology
  • CBE 6180: Advanced Molecular Thermodynamics
  • CBE 6210: Advanced Chemical Kinetics and Reactor Design
Computer and Information Science (CIS)
  • CIS 5600: Interactive Computer Graphics
  • CIS 5650: GPU Programming and Architecture
  • CIS 6800: Advanced Topics in Machine Perception
Electrical and Systems Engineering (ESE)
Mechanical Engineering and Applied Mechanics (MEAM)
Materials Science and Engineering (MSE)
  • MSE 5050: Mechanical Properties of Macro/Nanoscale Materials
  • MSE 5360: Electronic Properties of Materials
  • MSE 5750: Statistical Mechanics
  • MSE 6100: Transmission Electron Microscopy
  • MSE 6110: Advanced Synchrotron and Electron Characterization of Materials
Physics (PHYS)

Free Elective (1 C.U.)

Any technical course, subject to advisor approval.

  • Who do I contact for questions about my application?
    Please reach out to the admissions team at They can answer technical questions about your application or any technical difficulties you might run into. If you have other general questions about the program, you can reach out to Julia Esposito at
  • What are the prerequisites for this program?
    1. An undergraduate degree with a strong background in physical or theoretical sciences, engineering, or applied math
    2. Some experience with computer programming is strongly recommended
    If you have further questions, please check with Professor Talid Sinno who can comment on course content and qualifications.
  • Does SCMP offer any funding?
    We understand that a master’s degree is a significant financial endeavor. To review more funding information at Penn, visit our department webpages, and explore student employment opportunities. Applicants are also encouraged to consider federal funding available through submission of the FAFSA. While Penn Engineering generally does not provide financial assistance for master-level students, you can find information about a few funding opportunities on the Paying for Your Education page. All applicants are automatically considered for the scholarships listed by submitting an application for admission.
  • What do I need for my application?
    Our applications require the following:
    1. Resume
    2. Personal statement
    3. Two letters of recommendation, one of which is a faculty member familiar with your scholarly capabilities
    4. Unofficial transcripts from every university you earned course credit
    5. $90 application fee
    6. English Language Proficiency (for international students)
    7. GRE (optional; your application will not be hurt if you do not include it)
  • How do I apply to the SCMP Program?
    You can apply for SCMP using this link. You will need to create an account in our system. Then, fill out your information and upload all of your documents in the portal. Applications open on September 15th and are due on February 1st. You will hear back from us with a result by April 1st.