Research Intern (Academic Credit)

Cedars-Sinai Medical CenterLos Angeles, CA
Onsite

About The Position

A systems neuroscience research laboratory within the Department of Biomedical Sciences and Center for Neural Science and Medicine is seeking an unpaid Research Intern for academic credit. This internship provides a structured, hands-on educational experience in systems neuroscience, computational neurophysiology, and neural data analysis. The role is designed to build proficiency in core analytical workflows used in the laboratory while fostering collaborative research and developing strong technical skills in both electrophysiology and EEG-based approaches. The intern will support ongoing work in spike sorting of extracellular neurophysiology recordings and analysis of single-unit activity in rodent models of stroke recovery and sleep. The role will also contribute to characterization of neural modulation across the time course of motor recovery following stroke and development of proficiency in seizure detection through visual inspection of continuously recorded EEG data from ARX mutant animal subjects.

Requirements

  • Must be 18 years of age or older.
  • Must have a HS Diploma or GED.
  • Must reside in the greater Los Angeles area or surrounding cities while conducting all activities related to the intern or visitor appointment.
  • Those participating for academic credit must receive academic credit for the duration of the internship.
  • Must be receiving funding from an eligible sponsoring institution (employer, educator, funder, etc.) if a visitor.

Nice To Haves

  • Current undergraduate or graduate student pursuing coursework in neuroscience, biomedical engineering, computer science, psychology, biology, or a related scientific field.
  • Strong interest in systems neuroscience, computational neuroscience, electrophysiology, EEG analysis, or neural data analysis.
  • Familiarity with Python, data analysis software, or computational workflows.
  • Interest in translational neuroscience research involving motor recovery, sleep, and epilepsy models.
  • Strong analytical thinking and attention to detail.
  • Ability to learn technical workflows and follow research protocols consistently.
  • Strong organizational, documentation, and time management skills.
  • Comfortable working collaboratively in a laboratory research environment.
  • Ability to commit to a structured 10–12 week internship experience for academic credit.

Responsibilities

  • Complete all required institutional trainings, including HealthStream, CITI, IACUC/BAR requirements, and laboratory safety orientation.
  • Introduction to laboratory data infrastructure, including extracellular recording file formats, EEG data organization, and analysis software.
  • Exposure to Python, Kilosort/Phy, and custom EEG review tools.
  • Overview of rodent models of stroke recovery and sleep used in the laboratory.
  • Introduction to ARX mutant animal subjects used in EEG seizure studies.
  • Learn principles of extracellular neurophysiology recording and theoretical basis of spike detection and clustering.
  • Perform supervised spike sorting on existing recording datasets using automated pipeline tools.
  • Participate in mentored manual curation using Phy.
  • Apply quality control metrics, including isolation distance, L-ratio, and ISI violations, to evaluate and document single-unit yield across sessions.
  • Assist with analysis of sorted single units to characterize firing rate modulation and neural dynamics across post-stroke motor recovery.
  • Extend spike sorting and unit analysis to sleep session recordings to examine neural activity patterns during sleep and motor memory consolidation.
  • Begin training in visual inspection of continuously recorded EEG data from ARX mutant animal subjects for seizure detection.
  • Learn to identify ictal and interictal events in support of ongoing epilepsy research.
  • Conduct independent analysis activities with increasing autonomy under laboratory mentorship.
  • Support ongoing experimental studies through computational and data analysis contributions.
  • Summarize findings and participate in research discussions related to ongoing laboratory projects.
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