PhD in Computational Mathematics, Science and Engineering

This PhD program gives students broad and deep knowledge of the fundamental techniques used in computational modeling and data science, significant exposure to at least one application domain, and to conduct significant original research in algorithms and/or applications relating to computational and data science.  Students that have completed this PhD program will gain experience in the following:

• 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. 

A more detailed description of the PhD program requirements can be found at this page, and instructions on how to apply can be found at this page. The application deadline for Fall admission is January 2nd; applicants who wish to be considered for departmental, college, and University fellowships should ensure that all materials (including letters of recommendation and transcripts) have arrived by that date.  The application deadline for Spring admission is August 1st.  If you have questions, please contact the CMSE Graduate Director at cmsegrad@msu.edu.

Students in this PhD program will be able to work with CMSE faculty on topics including the following:

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

.