Ph.D. in Computational Mathematics, Science and Engineering

Applications for Fall 2026 are now open! Application deadline is January 2nd. Learn how to apply via link.

If you would like to be considered for departmental, college, and University fellowships, submit all materials (including transcripts and recommendation letters) by January 2nd.

 If you have questions, please contact the CMSE Graduate Director at cmsegrad@msu.edu.

The Ph.D. in Computational Mathematics, Science and Enginerring program gives students broad and deep knowledge of the fundamental techniques used in computational modeling and data science. The program aims to provide significant exposure to at least one application domain and an opportunity to conduct significant original research in algorithms and/or applications relating to computational and data science.  

The students in the CMSE Ph.D. program are able to:

• Analyze problems in terms of the algorithms and pre-existing computational tools, and engineer solutions using cutting-edge hardware and software.  
• Construct and implement models and simulations of physical, biological, and social situations, and use these models/simulations to understand experimental or observational data.
• Apply discipline-focused or methodology-focused topics in computational and data science to solve problems in the student’s application domain of choice.
• Conduct significant original research and present it in peer-reviewed articles, a written dissertation, and orally in a variety of venues. 

CMSE Students closely work with faculty to explore research in the following topics:

Scientific Computing

• Mesoscale electromagnetics
• Energy materials and phase filed models 
• Multidimensional stellar evolution
• Magnetohydrodynamics
• Protein-protein interactions
• Complex fluids and materials
• Galaxy formation and cosmological structure
• Seismic imaging and medical imaging
• Particle accelerators
• High power microwaves
• Low temperature plasmas
• Parallel and distributed computing

Computational Data Science

• Deep learning
• Signal processing
• Exascale algorithms
• Analysis and management of petascale datasets
• Machine learning
• High dimensional data analysis
• Convex optimization
• Bioinformatics
• Harmonic analysis
• Computational geometric and algebraic topology
• Interdisciplinary Computing Education Research