Charite Universitätsmedizin Berlin - Department of Experimental Neurology, Translational Neuromodulation Group
The Department of Experimental Neurology aims to create a bridge between basic research in neuroscience and clinical neurology. To this end, we carry out preclinical and clinical studies in order to transfer the knowledge gained from the laboratory to the bedside.
Call for a Student Assistant (m/w/d) (15 hours/week)
We are searching for a student assistant interested in stroke research! (60 hours/month for 8 months)
In order to develop new therapies for motor recovery after stroke, neural structures important for functional grasping and walking recovery post-stroke have to be determined through extensive motor testing in mice. We are searching for a student assistant that would perform motor tests such as the Staircase and Ladder Rung, in addition to daily animal handling and validation of the generated behavioral video data. Optionally, further tasks could include the evaluation and reprogramming of our deep learning-based MATLAB software, in case the candidate is interested in computational work as well. In our working group, teamwork, friendly atmosphere and respectful contact with animals are always encouraged. The candidate should start in February or March 2022 and will be paid a salary according to the "Tarifvertrag für studentische Beschäftigte" (TV Stud III, 12,96 EUR/h).
previous experience in laboratory animal handling
if possible EU Function A (formerly FELASA B) certificate or equivalent
enrollment as a full-time student
If you are interested in the position, please send a short cover letter and a CV including previous laboratory experience to: email@example.com
Please provide the documents in a single .pdf file (in English).
Hinweise zur Bewerbung:
Balkaya, Mustafa, et al. "Assessing post-stroke behavior in mouse models of focal ischemia." Journal of Cerebral Blood Flow & Metabolism 33.3 (2013): 330-338.
Mathis, Alexander, et al. "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning." Nature neuroscience 21.9 (2018): 1281-1289.