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Associate or Full Professor (with Tenure) in the Department of Neurosurgery specializing in computational modeling of neural recordings in humans and experimental animals

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University of Pennsylvania

University of Pennsylvania, Philadelphia, PA, USA

3 Star Employer Ranking

Associate or Full Professor (with Tenure) in the Department of Neurosurgery specializing in computational modeling of neural recordings in humans and experimental animals

Job Description

Location: Philadelphia, PA
Open Date: Oct 22, 2024
Deadline: Oct 22, 2026 at 11:59 PM Eastern Time

The Department of Neurosurgery at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for an Associate or Full Professor position in the tenure track. Expertise is required in the specific area of relating neural circuit dynamics to computational processes in the brain in the service of translational neuroengineering, including brain-computer interfaces. Applicants must have a Ph.D. or equivalent degree.

Teaching responsibilities may include teaching graduate students, medical students, residents and fellows.

Research or scholarship responsibilities may include leading a cutting-edge research program on computational modeling of neural recordings in humans and experimental animals to decipher the neural computations underlying movement, memory, and perception.

Preferred applicant must have federal funding as a contact PI.

We seek candidates who embrace and reflect diversity in the broadest sense. The University of Pennsylvania is an EOE. Minorities/women/individuals with disabilities/protected veterans are encouraged to apply.

Qualifications

Application Instructions

To apply, visit https://apply.interfolio.com/157270

Equal Employment Opportunity Statement

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.

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