Data and AI Researcher II, Department of Basic Medical Sciences (College of Medicine - Phoenix)

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.

Outstanding U of A benefits include health, dental, and vision insurance plans; life insurance and disability programs; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; retirement plans; access to U of A recreation and cultural activities; and more!
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• 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, and 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.

• 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

• Bachelor's degree in a field of science or similar degree.
• Previous research experience (internship, independent study).
• Experience with multimodal biomedical data integration (e.g., omics + imagine + text data).
• Familiarity with foundation models, generative AI, or clinical language models.
• Experience with natural language processing (NLP) pipelines and techniques.

Office of Human Resources, Talent Acquisition

talent@arizona.edu