Neuroscience Data Software Developer Associate

Stanford Reglab; California; Sur place; USD 90 000 - 140 000; Plein temps

Résumé du poste

Stanford University’s Solomon and Keller laboratories in Psychiatry and Behavioral Sciences seek a Software Developer Associate to build software for storing, managing, and analyzing human electrophysiological data. The role also supports software for brain stimulation experiments under supervision of the PI and collaboration with researchers and students.

The ideal candidate will contribute to Python-based tools, APIs, and interfaces, with emphasis on efficient data interaction and robust

Qualifications

• Bachelor's degree in a related field.
• Experience with Python and MATLAB for data analysis.
• Familiarity with handling neurophysiology or time-series data is preferred.
• Knowledge of privacy standards for health information.

Responsabilités

• Assess user needs for flexible access and analysis of electrophysiology data.
• Develop Python-based software packages and APIs for fast analysis of these data.
• Integrate software with in-house and Stanford data storage solutions.
• Refine existing software for brain stimulation experiments, including GUI development.
• Provide real-time troubleshooting during TMS experiments.
• Create user guides and tutorials and follow best practices for code documentation and version control.
• Follow team software development methodology.
• Suggest improvements to increase efficiency.

Connaissances

Python

MATLAB

Data analysis

Time-series data

Formation

Bachelor's degree

Outils

GitHub

Description du poste

Stanford University’s Solomon and Keller laboratories in Psychiatry and Behavioral Sciences seek a Software Developer Associate to build software for storing, managing, and analyzing human electrophysiological data. The role also supports software for brain stimulation experiments under supervision of the PI and collaboration with researchers and students.

The ideal candidate will contribute to Python-based tools, APIs, and interfaces, with emphasis on efficient data interaction and robust