Post-Doc Positions in CMSE

The Department of Computational Mathematics, Science and Engineering (CMSE) invites applications from outstanding candidates for (Postdoctoral) Research Associate-Fixed Term positions in the broad areas of Computational Modeling and Data Science.

The search is ongoing and depends on availability of funds.  The following is a list of Faculty and Groups that currently have funding available:


The O'Shea group is part of a collaboration that is developing an exascale version of the Enzo cosmology code, and is looking for a postdoctoral researcher who will perform research in theoretical galaxy formation, and will also participate in Enzo-E code development in support of this research.  The successful applicant will collaborate with researchers at MSU and in the Enzo collaboration, and will have the opportunity to lead their own projects.  The specific area of research is flexible, and depends on the candidate's interests (e.g., high redshift galaxy formation, the circumgalactic medium, quenching, dwarf galaxy evolution, etc.). There will also be opportunities to mentor undergraduate students in projects related to this work and to participate in other professional development activities such as teaching, grant-writing, and public outreach.  Applicants are expected to have a PhD in astrophysics, astronomy, physics, or a closely related field, and have significant expertise in computation. In addition, applicants should either have experience in running and analyzing astrophysical simulations of any sort or experience in parallel code development.  Knowledge of the C++ programming language is preferred.Experience with Enzo, Enzo-E, or adaptive mesh simulations is not required.  Review of applications will begin on December 16, 2019, and will continue until the position has been filled. 

The group is seeking to hire two postdoctoral researchers.  We work on developing new numerical methods to address challenging stiff multi-scale problems.  This includes working on developing:  new class of implicit methods that are A-Stable and avoids matrix inversion; the development of asymptotic preserving methods for transport; new efficient methods for non-Ideal Magnetohydrodynamic based on constrained transport; as well as new space time adaptive methods for the simulation of hyperbolic conservation laws.  Successful applicants working with Professor Christlieb will have a strong background in numerical analysis and scientific computing.

The Murillo group is developing new computational methods for modeling non-ideal plasmas using both high-performance computing and machine learning techniques.  Research will be carried out in the areas of magnetized ultracold plasmas, transport in non-ideal plasmas and implementation of new HPC methods in our MD code Sarkas. The successful applicant will work with Professor Murillo and other members of his group, and will have a strong background or interest in plasma physics, quantum mechanics, statistical mechanics, numerical methods, scientific computing, machine learning and multiscale modeling.

Alexei Bazavov [] The lattice QCD group at Michigan State University invites applications for a Postdoctoral Research Associate position starting in Summer or Fall 2020. The position is initially for one year with a possibility of extension to second and third year depending on the performance and availability of funds. The successful candidate will work in close collaboration with A. Bazavov at the Department of Computational Mathematics, Science and Engineering and the Department of Physics and Astronomy. Possible projects span a variety of topics in finite-temperature lattice QCD, as well as extensive software development for upcoming exascale architectures. Close collaboration with the Nuclear Physics group at Brookhaven National Laboratory (BNL) is envisioned with a possibility of spending some time of the appointment at BNL.

The labs of Dr. Elizabeth Munch ( and Dr. Dan Chitwood ( in the Department of Computational Mathematics, Science & Engineering at Michigan State University invite applications for a Research Associate-Fixed Term (Postdoctoral Researcher). The successful candidate will have previous experience in applying Topological Data Analysis (TDA) to scientific questions and working productively with mathematicians and computer scientists, as well as domain scientists without mathematical training. A focus of the research the candidate will undertake will be using TDA to quantify 3D, voxel-based images collected using X-ray Computed Tomography. The research team the candidate will join focuses on applying TDA to models of plant morphology, but the candidate will also work broadly across disparate fields of science, and a collaborative, diplomatic, team-oriented personality is a prerequisite for this position. Enthusiasm for education and outreach is a must, as in addition to publishing results, the candidate will disseminate TDA methods, code, data, and resources they develop to the scientific community in a reproducible, open, and innovative way. Above all, the candidate will have a passion for applying TDA approaches in the sciences and working with scientists in a productive manner to arrive at a working theory of applied topology in the sciences.

Review of applications will begin on December 16, 2019, and will continue until the position has been filled.

Matthew Hirn [] (CEDAR Team): The ComplEx Data Analysis Research (CEDAR) team, led by Prof. Matthew Hirn, anticipates having one or possibly two post doc openings. Members of the CEDAR team develop novel, cutting edge algorithms to analyze, organize, and leverage high dimensional data for a variety of tasks. Our approach to algorithm development is rooted in harmonic analysis, signal processing, geometry, graph theory, statistics, random processes, and machine learning, while placing equal emphasis on theoretical and applied research directions. At this time the group is working on problems in: (1) the mathematics of deep learning, particularly ConvNets and generative models; (2) machine learning and multiscale physics, specifically in the fields of quantum chemistry, materials science, and turbulence; (3) geometric and graphical models for high dimensional data analysis, including spectral graph theory and geometric deep learning; (4) optimal data fitting via smooth functions, with efficient algorithms; (5) quantum computing. See for more information on the group’s research. The preferred start date is August 16, 2020.


The research group of Dr. Jose Perea works on problems at the intersection of algebra, topology, geometry and data science.  The successful applicant will co-lead the development of novel methods for data analysis using tools from algebraic topology (e.g. fiber bundles, obstruction theory and spectral sequences), as well as their application to machine learning problems including non-linear dimensionality reduction, multimodal time series analysis and data fusion.  Candidates are expected to hold a PhD in mathematics at the time of appointment, have a strong background in algebraic topology and related areas, as well as coding proficiency in environments such as MATLAB and Python.  Inquiries can be directed to Dr. Jose Perea (, and more information can be found at

Multiple postdoc positions are available in Dr. Jianrong Wang's lab. Research in Dr. Wang’s lab is in the intersection of machine learning, statistical modeling, bioinformatics and computational biology, with a focus on building novel hierarchical Bayesian models and efficient learning algorithms to construct large-scale regulatory networks and to decode the genetic basis of human diseases, based on big-data from genomics. The candidates are expected to be highly self-motivated and have a strong passion for scientific research. Candidates should have (or expect to have soon) a PhD degree in related fields (bioinformatics, statistics, computer science, mathematics, etc.), have solid background in machine learning and statistical modeling, and have strong programming skills in Python or R. Candidates with prior research experiences in computational biology, bioinformatics, functional genomics, gene regulation, NGS data processing are preferred. Detailed information of Dr. Wang and his research can be found:

Applications are invited for a post-doctoral researcher in STEM education to join Michigan State’s Department of Computational Mathematics, Science, and Engineering (CMSE) to study introductory computational science courses. The courses focus on teaching computational modeling, data analysis, and programming. The researcher would work in CMSE on projects related to understanding student learning and engagement in these courses as well as be able to pursue their own research interests as they pertain to computational science education. The researcher will be supervised by Dr. Devin W. Silvia and will have the opportunity to collaborate with Dr. Danny Caballero, Dr. Brian O’Shea, and others both inside and outside of CMSE. There may also be opportunities to mentor undergraduate students in projects related to this work.