machine learning, deep representation learning, and real-world data modeling to stratify risk and optimize MHT formulations. The candidate must thrive in a multidisciplinary, fast-paced research environment and work independently and collaboratively across teams.
Outstanding UA benefits include health, dental, and vision insurance plans; life insurance and disability programs; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; retirement plans; access to UA recreation and cultural activities; and more!
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Duties & Responsibilities
- Conduct integration, analysis, and interpretation of large-scale human health datasets (e.g., UKBioBank, WHI, All of Us) using statistical and artificial intelligence/machine learning techniques.
- Develop and implement machine learning models, including unsupervised clustering and deep learning approaches, to identify precision treatment profiles and disease trajectories.
- Analyze research data and provide interpretations.
- Prepare manuscripts for publication and assist with grant applications.
- Present research findings at group meetings and national and international conferences.
- Graduate and undergraduate mentorship.
- Work collaboratively with other members and laboratories in CIBS.
- Additional duties as assigned.
Knowledge, Skills, and Abilities:
- Deep understanding of data science, AI/ML, and bioinformatics applications in biomedical research.
- Experience working with large EHRs, claims, or omics datasets.
- Ability to conduct research independently and within a team.
Minimum Qualifications
- PhD in data science, biomedical informatics, computational biology, neuroscience, or related field.
- Demonstrated experience in analyzing real-world medical data and applying machine learning techniques.
Preferred Qualifications
This position is part of a high-impact, data-driven research initiative focused on developing Precision Menopausal Hormone Therapy (P-MHT) strategies to reduce AD risk in women. The selected candidate will work at the forefront of translational neuroscience and women's health, leveraging multi-modal data-including genomics, EHR, and real-world clinical data-to build predictive models and inform therapeutic development. The role offers opportunities for collaboration with interdisciplinary teams in neuroscience, endocrinology, bioinformatics, and AI/ML. Demonstrated ability to independently prepare technical reports, visualizations, and manuscripts is preferred. Prior experience in neurodegenerative disease research, women's health, or pharmacogenomics. advantageous.
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: NIH salary guidelines, Depends on Experience
Compensation Type: salary at 1.0 full-time equivalency (FTE)
Type of criminal background check required: Name-based criminal background check (non-security sensitive)
Number of Vacancies: 1
Contact Information for Candidates
Gloria Bloomer, gbloomer@arizona.edu
Documents Needed to Apply: Curriculum Vitae (CV) and Cover Letter
Special Instructions to Applicant
If invited to interview, please be prepared to provide three (3) professional references.
