OPS - Undergraduate Student Assistant - College of Nursing

University of Florida; Gainesville (FL); On-site; USD 19,000 - 28,000; Part time

Job summary

The University of Florida College of Nursing is seeking an OPS-Undergraduate Student Assistant to support NLP, AI, and large-scale data analysis projects. The role involves text mining, data preparation, ML applications, and working with clinical datasets in HiPerGator and NavigatorAI environments.

Ideal candidates are enrolled in an undergraduate CS program with Python, NLP libraries, and data manipulation skills. Collaborative, multidisciplinary teamwork is required to drive research outcomes.

Qualifications

• Enrolled in an undergraduate computer science program.
• Experience in Python and NLP libraries (e.g., NLTK, SpaCy).
• Experience implementing NLP techniques.
• Ability to text mine large volumes of documents.
• Good communication, reliable, able to work in multidisciplinary teams.
• Experience with pandas and NumPy for data manipulation.

Responsibilities

• Assist with text mining and data preparation for NLP/AI research.
• Support analysis of clinical/research datasets in HPC environments.
• Implement NLP techniques and work with large language models.
• Collaborate with interdisciplinary research teams.
• Document data quality issues and transform tabular datasets.

Job description

OPS - Undergraduate Student Assistant - College of Nursing

• University of Florida
• United States
• Oct 30, 2026

Technology Casual

About University of Florida

At the University of Florida, we are a people of purpose. We're committed to challenging convention and ourselves. We see things not as they are, but as they could be. And we strive for a greater impact: one measured in people helped...

OPS - Undergraduate Student Assistant - College of Nursing.

Job Description:

The University of Florida College of Nursing is seeking a motivated OPS-Undergraduate Student Assistant to support research and data science initiatives involving natural language processing (NLP), artificial intelligence (AI), and large-scale data analysis. The selected student will assist with text mining, data preparation, machine learning applications, and the analysis of clinical and research datasets in high-performance computing environments such as HiPerGator and NavigatorAI.

Responsibilities may include processing and analyzing large volumes of text data, implementing NLP techniques and large language models (LLMs), identifying and resolving data quality issues, transforming and managing tabular datasets, and collaborating with interdisciplinary research teams. The ideal candidate will have experience with Python, pandas, NumPy, NLP libraries, and data-driven research methodologies. Experience with healthcare data, electronic health records (EHRs), GatorTron, and the UF Navigator AI Toolkit is highly desirable.

This position offers an excellent opportunity for undergraduate students interested in artificial intelligence, data science, biomedical informatics, and advanced computing applications to gain hands‑on research experience in a collaborative academic environment.

Duties include:

• Enrolled in an undergraduate computer science program
• Experience in programming languages, like Python, and NLP libraries (e.g., NLTK, Spacy)
• Experience in implementing natural language processing techniques
• Able to text mine large volume of documents
• Good communication skills, reliable, and able to work in multidisciplinary teams
• Experience working with pandas, NumPy, and strong data manipulation skills in transforming tabular datasets.
• Able to work with large datasets and identify data quality issues, inconsistencies, missing data, and unexpected values.

$17.00 hourly

Required Qualifications:

Required Qualifications: Enrolled in an undergraduate computer science program.

Preferred:

• Experience in implementing LLMs and GatorTron for text mining
• Experience working with clinical, healthcare, or electronic health record (EHR) data.
• Experience using the UF Navigator AI Toolkit, particularly its APIs for integrating LLMs into data-processing or research workflows.