Part Time Artificial Intelligence Research Assistant Jobs

Open Date 03/26/2026 Requisition Number PRN44571B Job Title Graduate Research Assist (RA) Working Title Graduate Research Assist (RA) Career Progression Track A00 Track Level FLSA Code Professional Patient Sensitive Job Code? No Standard Hours per Week 20 Full Time or Part Time? Part Time Shift Day Work Schedule Summary VP Area U of U Health - Academics Department 00952 - Division of Epidemiology Location Campus City Salt Lake City, UT Type of Recruitment External Posting Pay Rate Range per TBP guidelines Close Date 06/26/2026 Priority Review Date (Note - Posting may close at any time) 03/26/2026 Job Summary

• FTE: 0.5 FTE (approximately 20 hours/week); potential full-time effort during the summer semester.
• Duration: Initial one-year appointment beginning mid-May 2026.
• Tuition Benefit: Tuition support is available per university policy.
• Capstone/Thesis: This assistantship can support capstone or thesis projects aligned with the research program.
• Direct Report: To Dr. Senthil Nachimuthu, who will provide structured technical oversight and weekly task direction.

• Currently enrolled master's student in computing, informatics, data science, biomedical informatics, or a closely related discipline.
• Intermediate to advanced proficiency in Python and SQL.
• Experience applying NLP or machine learning methods to real-world datasets (e.g., clinical text, scientific text, or similarly complex unstructured data)
• Intermediate to advanced experience with Linux system administration (shell scripting, package management, user and service configuration).
• Ability to execute defined tasks independently while receiving structured technical guidance.
• Availability to attend on-site meetings or work in person for part of the scheduled time.
• This position requires eligibility for a VA appointment to access VA research systems (e.g., VINCI). Due to federal requirements, eligibility may be limited based on citizenship or visa status.

Responsibilities

• Develop, maintain, and optimize data processing pipelines for clinical and research datasets using Python and SQL. 
• Assist with the design, training, and evaluation of machine learning models applied to healthcare problems.
• Support research workflows within established Linux-based environments under technical supervision.
• Conduct exploratory data analysis and prepare visualizations and summary reports for research collaborators.
• Contribute to manuscript preparation, documentation, and reproducible research practices.
• Collaborate with clinical and research faculty, staff, and other trainees.
• Work within secure, access-controlled computing environments (e.g., VA VINCI, CHPC) following data governance and compliance requirements

Minimum Qualifications Preferences

• Experience using or modifying machine learning models such as decision trees, Bayesian models, deep learning architectures, transformers, or LLMs.
• Familiarity with healthcare data standards (e.g., SNOMED CT, LOINC, RxNorm) or electronic health record systems.
• Experience with version control (Git/GitHub) and collaborative software development practices.

Type Benefited Staff Special Instructions Summary Additional Information

The University is a participating employer with Utah Retirement Systems ("URS"). Eligible new hires with prior URS service, may elect to enroll in URS if they make the election before they become eligible for retirement (usually the first day of work). Contact Human Resources at (801) 581-7447 for information. Individuals who previously retired and are receiving monthly retirement benefits from URS are subject to URS' post-retirement rules and restrictions. Please contact Utah Retirement Systems at (801) 366-7770 or (800) 695-4877 or University Human Resource Management at (801) 581-7447 if you have questions regarding the post-retirement rules.
This position may require the successful completion of a criminal background check and/or drug screen.
The University of Utah values candidates who have experience working in settings with students and possess a strong commitment to improving access to higher education.
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