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Job Posting #129

Posting #129 Position Overview
Organization Name


Scientific Computation Research Center, Rensselaer
Polytechnic Institute


Job Title

Post Doctoral Research Associate


Troy, NY


The Scientific Computation Research Center  ( at Rensselaer is seeking highly qualified post-doctoral research associates to develop parallel adaptive unstructured mesh technologies that will be applied in multiple areas of application including fusion modeling, computational fluid dynamics and others.

The successful candidates will develop and implement parallel algorithms that effectively operate on the current and future heterogeneous massively parallel computer systems to support unstructured mesh methods for particle-in-cell  methods, mesh adaptation, simulation driven evolution of geometry and meshes over complex domains, etc. The  successful candidates must be able to interact with faculty, research staff and students in the Scientific Computation Research Center. The successful candidates will also be required to interact with sponsors.


Minimum qualifications:
• PhD in Engineering, Applied Mathematics,  Computer/Computational Science, or related discipline.
• Have expertise in a subset of the areas listed and be interested in working closely with others that provide expertise in the other areas:
     • Unstructured meshing generation/adaptation  technologies,
     • Development and optimization of parallel Particle in Cell methods (PIC),
     • Development of parallel unstructured simulation technologies.
• Experience in parallel programming and high performance computing.
• Good knowledge of FORTRAN, C and/or C++ programming languages and GNU/Linux operating system is required.
• Knowledge of modern software engineering tools will be considered favorably.

Application Procedure


Interested applicants should send a copy of their latest CV with a cover letter or email, names of at least three  references, and a summary of recent work and their source code from work in a related field (repositories in GitHub, Bitbucket, or GitLab are strongly preferred). All applications should be submitted electronically and addressed to Prof. Mark Shephard (


Contact Info

Prof. Mark Shephard (