Postdoc position to develop computational methods for COPASI.

Posted on: July 12, 2019.

This position has now been filled.


The postdoctoral fellow will work on an NIH-funded project that develops the widely used simulation software COPASI. This project is focused on parallel optimization algorithms for high-performance computing to accelerate parameter estimation of biochemical models. The postdoc will contribute to the C++ source code of COPASI and the python code of Cloud-COPASI. The postdoc will join the International team that are developing COPASI with collaborators in the University of Virginia and University of Heidelberg, Germany.

This position has now been filled.

Requirements: A PhD or equivalent in computational biology, computer science, mathematics, physics, or related areas. Experience of programming in C++ or python are essential. Previous experience of using COPASI or other systems biology simulation software is desirable but not essential.

Availability: this positon is available immediately and open until filled. Contact Pedro Mendes for further details. Information about Postdoc positions at UConn Health.

Further details: COPASI is a software application for simulation and analysis of biochemical networks and their dynamics. It is routinely used in biological and chemical research, as documented in many publications from authors around the world (e.g. in 2018 COPASI was used in 80 publications). The development team also supports the bioscience community of users through online interaction and workshops. The project will continue to support interoperability and standards compliance, particularly regarding the systems biology markup language (SBML) and the simulation experiment description language (SED-ML). COPASI is an International project with a geographically distributed team (with collaborators in the University of Virginia and University of Heidelberg, Germany) and thus requires frequent online communication and extensive use of GitHub.

The environment at UConn Health is particularly rich in the area of computational biology and the successful candidate will be able to interact with other teams working on systems biology, computational biology and bioinformatics, including related computational biology projects (VCell, BioNetGen, etc.).

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