Connectionists: Postdoctoral positions in physiological and network analyses of Alzheimer's mouse models

[Hasenstaub, Andrea]

Postdoctoral position: The role of hearing loss in cognitive impairment and Alzheimer’s disease pathology


A full-time post-doctoral position is available in the Coleman Memorial Laboratory of at the University of California, San Francisco. The appointment is funded by a newly awarded NIH R01 grant to study potential contributions of hearing loss to cognitive impairment using a mouse model of Alzheimer’s disease. The project combines computational analyses of cell and network interactions with large-scale in vivo physiology, brain-wide histological analysis, and behavioral training. The principal investigator for the project is Dr. James Bigelow, with co-investigators Drs. Andrea Hasenstaub and Christoph Kirst. To apply, please send a CV and brief statement of interest to james.bigelow at ucsf.edu. Applications will be considered on a rolling basis.


Minimum qualifications:

  *   PhD in neuroscience, physiology, cell biology, biomedical engineering, or related field.

  *   Strong written and oral communication skills demonstrated by peer-reviewed publications and conference presentations.

  *   Interest and ability to compete for extramural funding.



Preference will be given to candidates with experience in one or more of the following areas:

  *   In vivo physiology, especially acute and/or chronic recordings using multichannel electrode arrays (e.g., Neuropixels). Specific experience within the auditory cortex or hippocampal complex is desirable.

  *   Histological techniques, especially tissue clearing and brain-wide histological analysis using iDISCO or related pipelines.

  *   Behavioral assessment in Alzheimer’s model mice (e.g., spatial learning and memory)

  *   Hearing assessment via auditory brainstem response measurements.

  *   Computational techniques for assessing functional interaction among neurons within and between brain regions (e.g., Granger causality, ensemble detection).

  *   Analyzing large, complex datasets using MATLAB or Python.


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