Post-Doc Positions in CMSE

Notice: All CMSE postdoc applicants should apply to position #481898 via MSU’s online job application website:

Applicants must specify an area of focus and faculty they want to work with in the cover letter of their application. Applicants must provide a Research Statement, Teaching Statement, Curriculum Vita, and three references. Applicants must apply through the MSU online system.

The Department of Computational Mathematics, Science and Engineering (CMSE) invites applications from outstanding candidates for fixed term Postdoctoral Researchers 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 for Postdocs:


Prof. Ravishankar leads research at the intersection of signal processing, computational imaging, machine learning, large-scale optimization, theory, and applications. The postdoc will work on developing new models, machine learning methods, algorithms, and theory for imaging (MRI, CT, and other computational imaging systems) and signal processing applications. Candidates should have (or expect to have shortly) a PhD degree in Electrical or Computer Engineering, Biomedical Engineering, Mathematics, or related fields, and should have strong programming skills.

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 postdoc positions will be supported by NIH. Detailed information of Dr. Wang and his research can be found:

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.

Application materials:

  • CV;
  • Statement of prior research experiences and future research interests (<=2 pages);
  • Two publications (as first author or co-first author) from prior research;
  • Contact information of 3 references.

If you are interested, please send an email to Dr. Wang ( with your application materials.


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 application will work with Prof. 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.

Research in developing and applying data analytics and computational algorithms to enable fast and high-fidelity multi-scale seismic imaging, with emphasis on dynamic monitoring the near-subsurface structure of the Earth. Potential projects include but are not limited to assimilating seismic data sets derived from passive, active, and ambient noise sources to image, monitor, and model (1) the spatial and temporal changes of active seismic and volcanic regions and (2) the interactions of water, life, soil, and rock in the Earth's critical zone. Candidates with strong background in seismic imaging and high-performance computing will be given priority. Experience with large data sets, machine learning, and GPU programming are desired. The successful candidate will be working with Dr. Min Chen. Preferred start date no later than September 1st, 2019, earlier if possible.

Applicants must specify an area of focus and faculty they want to work with in the cover letter of their application. Applicants must provide a Research Statement, Teaching Statement, Curriculum Vita, and three references. Applicants must apply through the MSU online system.

Postdoc position open in the field of inverse problems and imaging interpreted in the broad sense. These include, but not limited to, medical/radar/seismic imaging, image processing, data-driven inverse problems, and so forth. Candidates are expected to have expertise in one or more of the following areas: theoretical and computational partial differential equations, microlocal analysis, stochastic partial differential equations, statistical methods in inverse problems, optimization, numerical simulations. Proficient programming skill is strongly recommended. Successful applications should be well motivated, have strong written and oral communication skills, and be able to conduct research of high quality both independently and collaboratively. The selected candidate will join the Inverse Problems and Imaging group at CMSE and collaborate with Dr. Yang as well as other faculty members and graduate students.

We are working on developing a new class of implicit methods that avoids matrix inversion.  The method is based on expanding operators with a method we developed based on successive convolution and fast kernel tricks.  The base scheme is as fast as an explicit operator, but A-stable. We are working on expanding this methods to a range of Partial Differential Equations. Successful applicants working with Professor Christlieb will have a strong background in numerical analysis and scientific computing.

We are working on building theoretical understanding and practical computational implementations of metrics for graph-like signatures arising in the field of Topological Data Analysis.  Specifically, the postdoc will be working on the interleaving distance for Reeb graphs and Mapper.  The successful applicant working with Dr. Munch will have a strong mathematical and/or theoretical computer science background, as well as comfort with programming and implementation, preferably in python.

The project we will be working on is achieving super-resolution in inverse problems. It consists of a  class of problems arising from seismic and medical imaging where sharp reconstructions/images are desired from band-limited observations. We utilize applied and computational harmonic analysis with an emphasis on compressed sensing and convex optimization theory. Prior experience in inverse problems would be a plus. The successful candidate will be working with Dr. Rongrong Wang.  Preferred starting date no later than Sep 1, 2018.