Job Description

• Job Type: Support Staff - Union
• Bargaining Unit: SSA
• Regular/Temporary: Temporary
• End Date if Temporary: 1 year
• Hours Per Week: 35
• Standard Work Schedule: 9am-5pm
• Building:
• Salary Range: $59,845.49- $59,845.49

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.

Position Summary

At the Stavros Niarchos Foundation (SNF) Center for Precision Psychiatry & Mental Health, we envision a near future when mental illness is treated in novel, targeted, and tailored ways, based on a new understanding of how mental illness develops and persists, unique to each individual. Our mission is to apply advances in neuroscience, technology and precision medicine to psychiatry; to create breakthroughs that further our understanding of the biological causes and underpinnings of mental illness; and to discover treatments that alleviate suffering from conditions previously considered untreatable. By addressing biological causes, and identifying genetic and other biological markers, the SNF Center will eliminate the stigma of mental illnesses and address societal disparities by ensuring equitable access to breakthrough Precision Psychiatry treatments.

The Research Assistant will play a key role in investigating functional connectivity in two transgenic mouse models of schizophrenia and their control groups. This position requires advanced analysis of neural data, contextualizing connectivity analyses with behavioral measurements, and developing innovative methods to improve data compatibility and reveal biologically relevant patterns. The candidate will work on integrating multiple measures of connectivity, including phase locking value, wavelet transforms, and Granger causality, focusing on frequency bands and regions of interest related to sensory-evoked activity and resting-state functional connectivity. Additionally, the candidate will develop an epoch-free pipeline that leverages dimensionality reduction and hidden semi-Markov models with behavior-dependent priors, aiming to uncover dynamic brain state transitions linked to behavior.

Located in the heart of Manhattan in New York City, the Zuckerman Institute houses over 50 laboratories employing a broad range of interdisciplinary and diverse approaches to transform our understanding of the mind and brain. In this highly collaborative and inclusive environment, experimental, computational, and theoretical labs work together to gain critical insights into how the brain develops, performs, endures and recovers.

Responsibilities

Duties involve the following tasks:

• Data preparation
  • Implement LocaNMF functional parcellation and signal extraction on remaining sessions and compare to previous data for inter / intra subject similarity.
  • Extract remaining whisking motion from ROI using Face Map and align all data.
  • Pre-process videos again after removing outlier frames and optimize hemodynamic correction to properly discern between data channels in aberrant sessions.
• Sensory-evoked task
  • Quantify neural activity peak attenuation after consecutive whisker stimuli.
  • Measure the neural activity peak latency after whisker stimuli.
• Data analysis
  • Correlate Pearson correlation values between regions of interest to genotype, sex and day. Epoch these measurements using behavioral syllables or states.
  • Extract common behavioral syllables from pupil diameter, whisking, and locomotion measurements using Keypoint Moseq.
  • Continue to apply deep learning Granger causality model for directed connectivity measurement concurrent to pose estimation.
  • Train deep learning model on correlation between neural and behavioral data.
  • Optimize PCA, ICA, clustering, and hidden semi-Markov model to derive common connectivity profiles, as well as transition probabilities and durations between these states, in relation to behavioral epochs.

Minimum Qualifications

• Bachelor???s degree with at least 1.5 years of experience. 
• The student is expected to have fluency in Python and Matlab with experience in code writing, pipeline building and debugging as editor/writer. Data visualization skills are key.
• Ability to apply statistical tools will be necessary for the fulfillment of the task.

Preferred Qualifications

Familiarity with image processing data analysis is preferable.

This is a short-term appointment hire for no more than 1 year.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

About Columbia University

Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis. It seeks to attract a diverse and international faculty and student body, to support research and teaching on global issues, and to create academic relationships with many countries and regions. It expects all areas of the university to advance knowledge and learning at the highest level and to convey the products of its efforts to the world.

Job Tags

Similar Jobs