Data and AI Researcher II, Department of Biomedical Informatics (College of Medicine - Phoenix) Posting Number req26596
Department COM Phx Biomedical Informatics
Department Website Link Location Greater Phoenix Area
Address 475 N 5th Street, Phoenix, AZ 85004 USA
Position Highlights The University of Arizona College of Medicine - Phoenix (COM-P) is seeking a highly motivated candidate for the full-time position of Data and AI Researcher II in Dr. Peipei Ping's laboratory. Our lab develops integrative omics and AI-driven data science approaches to uncover how genes, proteins, and their dynamic interactions shape cardiovascular function and disease, with the goal of transforming large-scale biological and clinical data into actionable insights for diagnosis, treatment, and scientific discovery.
The ideal candidate should have a strong background in computational biology, data science, and/or omics analysis, with experience in large-scale biomedical data integration. The individual will develop and apply analytical and machine learning methods to interrogate complex multi-omics, imaging, and clinical datasets, contributing to research in cardiovascular systems biology and precision medicine. The Data and AI Researcher II will benefit from the highly collaborative and interdisciplinary environment within Dr. Peipei Ping's lab and its broader national partnerships in AI-driven biomedical research.
- Visa sponsorship is not available for this position.
Duties & Responsibilities - Analyze multi-omics, imaging, and clinical datasets using established analytical and statistical methods.
- Contribute to ongoing studies in cardiovascular systems biology, including exposure to proteomics, transcriptomics, and integrative data analysis approaches.
- Interpret results and collaborate in preparing summaries, presentations, and draft materials for publications.
- Review and apply existing computational workflows to support reproducible and well-documented data analysis.
- Provide support for the use and maintenance of computational pipelines for multi-modal biomedical data.
- Apply existing machine learning and data analysis methods under guidance from senior team members.
- Work with the PI and team to improve organization, usability, and documentation of analytical tools and workflows.
- Document workflows, software usage, and standard operating procedures (SOPs) to support reproducibility and team knowledge sharing.
- Collaborate with interdisciplinary teams, including researchers in computational biology, clinical research, and data science.
- Contribute to preparing reports, manuscripts, and grant-related materials, including coordination and formatting support.
- Present summaries of work and project updates in lab meetings and collaborative settings, with guidance as needed.
Knowledge, Skills & Abilities:- Ability to communicate in a clear, concise manner orally and in writing.
- Detailed oriented, conscientious, and able to follow instructions.
- Knowledge of the principles and techniques of the subject discipline.
- Knowledge of modern research methods, data collection and analyses.
- Skill in analyzing and evaluating data.
This job posting reflects the general nature and level of work expected of the selected candidate(s). It is not intended to be an exhaustive list of all duties and responsibilities. The institution reserves the right to amend or update this description as organizational priorities and institutional needs evolve.
Minimum Qualifications - Bachelor's degree or equivalent advanced learning attained through professional level experience required.
- Three (3) years of relevant experience, or equivalent combination of education and work experience.
Preferred Qualifications- Bachelor's or Master's degree in data science, computer science, biomedical engineering, computational biology, or a related field.
- Have completed relevant coursework in data science, applied mathematics, machine learning, or computational methods.
- Previous research experience (internship, independent study).
- Experience with multimodal biomedical data integration (e.g., omics + imaging + text data).
- Familiarity with foundation models, generative AI, or clinical language models.
- Experience with natural language processing (NLP) pipelines and techniques.
- Familiarity with ethical, legal, and social implications of AI.
- Experience with grant writing for independent and research center funding.
FLSA Exempt
Full Time/Part Time Full Time
Number of Hours Worked per Week 40
Job FTE 1.0
Work Calendar Fiscal
Job Category Research
Benefits Eligible Yes - Full Benefits
Rate of Pay $75,540 - $98,201
Compensation Type salary at 1.0 full-time equivalency (FTE)
Grade 10
Compensation Guidance The
Rate of Pay Field represents the University of Arizona's good faith and reasonable estimate of the range of possible compensation at the time of posting. The University considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate's work experience, education/training, key skills, and internal equity.
The
Grade Range represent a full range of career compensation growth over time. The university offers compensation growth opportunities within its career architecture. To learn more about compensation, please review our Applicant Compensation Guide and our Total Rewards Calculator.
Career Stream and Level PC2
Job Family Research & Data Analysis
Job Function Research
Type of criminal background check required: Name-based criminal background check (non-security sensitive)
Number of Vacancies 1
Target Hire Date Expected End Date Contact Information for Candidates Office of Talent Acquisition
[email protected]Open Date 7/22/2026
Open Until Filled Yes
Documents Needed to Apply Resume and Cover Letter
Special Instructions to Applicant Application: The online application should be completed in its entirety. Blank or missing information may be considered an incomplete submission.
Cover Letter: Should clearly indicate how your skills and professional employment experience meet the Minimum and the Preferred qualifications (if applicable).
