A position for a post-doctoral fellow in the laboratory of Simon Rumpel, University Medical Center of the Johannes Gutenberg University Mainz, Germany is available. It is part of a collaborative research project conducted jointly with the Institute of Molecular Biology (IMB) in Mainz and a partner from industry.
Development of advanced promoter elements for future gene therapy vectors
Vector technology for therapeutic applications is rapidly gaining importance. A core element of any vector is the promoter controlling the expression of the gene of interest. Future promoter elements will be selected upon their ability to drive gene expression at therapeutic levels in specific target cells.
The aim of this project is to identify genomic control elements suitable for the design of small, powerful artificial promoters in the context of Adeno-associated viruses (AAV). Towards this end, we recently established efficient methodology allowing to identify genomic sequences in in vivo model systems, including the mouse brain. This project relies on the tight interaction between scientists with experimental and bioinformatics background.
Applicants have obtained a PhD in Molecular Biology or related field, have excellent communication skills and have an interest to work in a collaborative and international environment. Profound knowledge in advanced molecular biology and cloning is essential. Additional experience in AAV biology and vector production is preferred. This position offers an exciting career opportunity at the interface of basic and applied research.
For further information, or to apply, please contact Simon Rumpel (email@example.com). Applications should include a motivation letter, the applicant’s CV, list of publications and contact information for two references.
A position for a PhD-Student funded as part of the DFG Priority Program SPP 2041 ‘Computational Connectomics’ is available in the laboratory of Simon Rumpel at the University Medical Center of the Johannes Gutenberg University Mainz, Germany.
The dynamic connectome: dynamics of learning
The connectome of the cerebral cortex is highly dynamic, exhibiting 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 to 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 modeling to help answer this fundamental question.
Applicants should have obtained a master’s degree or diploma in neuroscience or 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 modeling efforts.
For further information, or to apply, please contact Simon Rumpel (firstname.lastname@example.org). Applications should include a motivation letter, the applicant’s CV, publications and email addresses of two references.
A position for a student assistant (studentische Hilfskraft) in the laboratory of Simon Rumpel, University Medical Center of the Johannes Gutenberg University Mainz, Germany is available.
Image and data processing in neuroscience
Many current studies in neuroscience rely on the analysis of large datasets acquired in chronic recordings from the brain. Our group investigates the long-term dynamics of synapses and activity patterns in the auditory cortex of mice using in vivo two-photon microscopy. Towards this end, we develop custom pipelines for the processing of primary and secondary data.
Student assistants will team up with a scientist in the lab and tackle a specific aspect of a project involving data analysis. This work primarily involves programming of software routines and can be done from home. However, we encourage the student assistants to use the possibility to often visit the lab.
Applicants are students of computer science, physics or related subject and have experience in data handling and programming in Matlab or Python. Working time is flexible and will be compensated according to the salary scheme for a student assistant. Working in the team offers the chance to get immediate insight in current neuroscience projects on the dynamics of the brain.
For further information, or to apply, please contact Simon Rumpel (email@example.com). Applications should include a CV.