Job Description

Through this internship, interns will gain hands-on training in eye-tracking, pupillometry, and autonomic data acquisition in clinical research settings, along with advanced skills in quantitative data preprocessing, feature extraction, and analysis using MATLAB, R, and Python. Interns will learn how to apply AI and machine-learning methods to multimodal physiological data for patient–control differentiation, including model evaluation and interpretation. The internship also provides practical experience in reproducible research workflows, interdisciplinary collaboration, and scientific communication, preparing interns for graduate study or research-oriented careers in neuroscience, psychology, biomedical engineering, or data science.

Preferred Intern Educational Level

• Undergraduate or Master’s-level students
• Preferred backgrounds: Biomedical Engineering, Neuroscience
• International students interested in research training, clinical neuroscience, or graduate study are especially encouraged to apply

Skill sets or Qualities

Strongly required / highly prioritized:

• Strong quantitative and analytical skills, with demonstrated ability to work with multidimensional behavioral and physiological data
• Proficiency in MATLAB for data processing, visualization, and statistical analysis (required)
• Working knowledge of R and/or Python for statistical modeling, data wrangling, or reproducible analysis pipelines
• Demonstrated experience handling eye-tracking or behavioral (or physiological) data
• Ability to read, modify, and write analysis scripts independently, not only run existing code

AI / data science–related skills:

• Experience applying machine learning or AI-based analytical approaches to behavioral, physiological, or biomedical data
• Familiarity with supervised learning methods for classification or prediction (e.g., patient vs. control differentiation)
• Experience with feature extraction, feature selection, and model evaluation (e.g., cross-validation, ROC/AUC analysis)
• Ability to integrate multimodal features (e.g., eye-tracking, pupil, autonomic indices) into AI/ML pipelines
• Practical experience using AI-related libraries or toolboxes (e.g., MATLAB toolboxes, Python-based frameworks such as scikit-learn)

Highly desirable:

• Prior experience with eye-tracking systems
• Experience analyzing pupillometry and oculomotor metrics (e.g., pupil dilation, microsaccades, saccades, blink measures)
• Familiarity with advanced statistical models (e.g., linear mixed-effects models, time-series or mass-univariate analyses)
• Experience maintaining clean, reproducible, and well-documented analysis workflows

Personal qualities:

• Comfortable working in a data- and computation-intensive research environment
• Strong attention to detail and commitment to data quality and reproducibility
• Ability to work independently while communicating clearly with supervisors and collaborators
• Strong motivation to pursue advanced training or graduate study in neuroscience, psychology, or data science