Post-DocApplications Closed

Dr. Peter Petersen

Copenhagen
Apply by Oct 1, 2021

Application deadline

Oct 1, 2021

Job

Job location

Dr. Peter Petersen

Geocoding

Copenhagen

Geocoding in progress.

Source: legacy

Quick Information

Application Deadline

Oct 1, 2021

Start Date

Flexible

Education Required

See description

Experience Level

Not specified

Job

Job location

Dr. Peter Petersen

Geocoding

Copenhagen

Geocoding in progress.

Source: legacy

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Job Description

We are seeking a highly motivated postdoctoral fellow for a project addressing the generation and functions of theta oscillations in spatial navigation using systems neuroscience and population-level approaches. The research will take place at the Department of Neuroscience (in.ku.dk) at University of Copenhagen in the lab of Dr. Peter C. Petersen (PetersenLab.org).

The project involves performing electrophysiological recordings from freely moving animals using chronically implanted high-density Neuropixels silicon probes and applying optogenetics for single cell tagging, and behavioral manipulations.

Learn more about the position and the application process here: https://employment.ku.dk/faculty/?show=157309

Requirements

  • Profile
  • We are looking for a highly motivated and enthusiastic scientist with the following competencies and experience:
  • Essential experience and skills:
  • You have a Ph.D. in Neuroscience or other related sciences.
  • You are experienced in electrophysiology.
  • You have an active interest in the brain’s memory mechanisms.
  • Experience with data analysis of single cells
  • Proficient communication skills and ability to work in teams
  • Excellent English skills written and spoken
  • Desirable experience and skills:
  • In-vivo electrophysiological experiments in rodents
  • Knowledge of and experience with optogenetics/virus/genetic techniques and tools.
  • Data analysis using Matlab or Python applied to single cells and populations (e.g. spike sorting and LFP analysis).
  • Computational neuroscience with knowledge in applying dimensionality reduction methods and population-level approaches.