Researcher - Artificial Intelligence and Machine Learning

Details•San Diego, CA

7d•Onsite

About The Position

The University of San Diego's Shiley-Marcos School of Engineering is seeking a researcher to contribute to an NSF-funded project focused on data-centric artificial intelligence (AI) and wearable biosensors for enhancing student learning, engagement, and stress management. This project combines wearable physiological sensing with machine learning and data-centric AI methods to develop reliable models of student engagement and stress, ultimately providing meaningful, data-driven insights for educators. The researcher will work closely with the Principal Investigator, Dr. Nadieh Moghadam, and other members of the research team. This is a full-time or part-time temporary position with an anticipated end date of August 31, 2027, renewable at the discretion of the University based on performance and funding.

Requirements

Bachelor’s degree or higher in Electrical/Computer Engineering, Computer Science, Data Science, Biomedical Engineering, or a related field.
Programming experience in Python, MATLAB, or similar environments.
Background or demonstrated interest in machine learning, artificial intelligence, signal processing, or data analysis.
Strong analytical, communication, and problem-solving skills.
Ability to work independently and collaboratively within a research team.
Successful completion of a pre-employment background check.
Persons offered employment in this position will be required to provide official education transcripts for degree verification purposes.

Nice To Haves

Experience with machine learning/deep learning frameworks.
Experience analyzing physiological or time-series data.
Familiarity with wearable sensors, biosignal processing, or data-centric AI.
Experience with app/software development or data visualization.
Previous research experience and/or scholarly publications.

Responsibilities

Processing and analyzing physiological signals from wearable biosensors, including heart rate and electrodermal activity (EDA).
Developing, implementing, and evaluating machine learning and AI models using public and project-generated datasets.
Applying data-centric AI techniques, including data cleaning, feature engineering, augmentation, and multimodal data analysis.
Evaluating and working with wearable sensing technologies.
Supporting development and evaluation of an adaptive application for translating AI results into actionable insights for instructors.
Support human-subject research activities, including participant recruitment, wearable biosensor deployment, physiological data collection during classroom studies, and organization and analysis of collected data in accordance with the approved IRB protocol.
Conducting literature reviews and documenting research methods and results.
Contributing to research publications, presentations, and dissemination of project findings.
Collaborating with undergraduate researchers and other members of the research team.