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

Employer
UCSF
Location
San Francisco, California
Salary
Compensation specified by the UCSF postdoc salary scale (https://postdocs.ucsf.edu/pay-and-benefits)
Closing date
Dec 19, 2022
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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 large-scale in vivo physiology, brain-wide histological analysis, behavioral training, and computational techniques. 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@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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