Postdocs: cardiac ICN modeling 

Two Cardiac Simulation/Computational Physiology postdoc or contractor computer modeling positions are available for development of a novel ICN  (intrinsic cardiac nervous system)  neuronal network simulation.


New NIH SPARC Initiative ICN project is being launched at the SUNY Downstate Brooklyn Neurosimulation lab ( We plan to build the first detailed model of RAGP (right atrial ganglionic plexus) The work is being done in collaboration with Jefferson University (Philadelphia, CT) under the auspices of a 2-year NIH SPARC Initiative grant. Candidates should have a strong background in computer simulation, with demonstrated proficiency in Python. Strong interest, though not necessarily research experience, in neuro- or cardiac science is a must. Some experience with NEURON ( and NetPyNE ( is desirable.

Either a postdoctoral fellowship position or research contractor position can be arranged.

Some excerpts from the grant application:

Project title: Modeling network dynamics of cardiac right atrial ganglionic plexus to enable in silico testing of vagal neurostimulation strategies

Priority being addressed: Interoperable, extensible, and personalizable simulations on the o2S2PARC platform to inform bioelectronic medicine development, with particular focus on diseases and conditions that impact the heart.

Project Overview: The primary objective of the project is to develop computational models of neurons and networks of the intrinsic cardiac nervous system (ICN), implement simulations on the o2S2PARC simulation platform, in order to better understand how vagal inputs influence the local cardiac circuits, and improve neuromodulatory medicine for heart disease. The project will follow a sequence of increasing model complexity of the neurons and neural networks forming the right atrial ganglionic plexus (RAGP) within the ICN, beginning with neuronal electrophysiology, building on these to add neuromodulatory function and heterogeneity based on molecular phenotypes, and then connecting these in networks examining their contributions to overall ICN dynamics for specific predictions to control the heart. These models will account for species differences (rat vs. pig vs. human) and sex differences.

It is now feasible to develop RAGP-ICN neuronal models incorporating the specific anatomical, connectional and molecular diversity of the system in such a way as to directly predict approaches to neuromodulatory therapy development. This has become feasible due to the current emergence of comprehensive and foundational data on the system, including RNAseq and single neuron transcriptomic data suggesting neuropeptidergic signaling driven paracrine networks to explore in simulation. Combining these data with state-of-the-art computational neuroscience repositories will produce modeling resources to inform and widely explore neuromodulatory therapy opportunities at the heart within the next 4 years. 

For more information:

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