Job ID: 12511

Ph.D. Student in Mainz/Germany

Position: Ph.D. Student

Deadline: 1 October 2021

City: Mainz

Country: Germany

Institution: University Medical Center, Johannes-Gutenberg-University Mainz

Department: Institute of Physiology, FTN

Description:

The dynamic connectome: dynamics of learning

A position for a PhD-Student funded as part of the DFG Priority Program SPP 1080 ‘Computational Connectomics’ is available in the laboratory of Simon Rumpel at the University Medical Center of the Johannes Gutenberg University Mainz, Germany.

The connectome of the cerebral cortex is highly dynamic, exhibiting a high turnover of synaptic connections even under basal conditions. Nevertheless, our brains are able to maintain life-long memories. How are such memories formed and maintained in such a dynamic environment?

The aim of this project is to unravel how neuronal circuits are able to adapt and incorporate new information while simultaneously maintaining functional stability. To that goal, we will combine time lapse imaging of excitatory and inhibitory synaptic connectivity of rodent cortex during learning with high-throughput automated data analysis and computational modelling to help answer this fundamental question.

Applicants should have obtained a master’s degree or diploma in neuroscience or a related field, have a quantitative understanding of biology, have excellent communication skills, and have an interest to work in a collaborative and international environment. Previous experience in physiological and imaging approaches are beneficial. This project will be conducted in tight collaboration with Matthias Kaschube’s and Jochen Triesch’s groups at the FIAS in Frankfurt, which will complement the experimental approaches in Mainz with advanced image analysis and theoretical modelling efforts.

For further information, or to apply, please contact Simon Rumpel (sirumpel@uni-mainz.de). Applications (single PDF) should include a motivation letter, the applicant’s CV, list of publications and contact information of two references.

previous job ID: 28733