Assistant Scientific Investigator, Center for Innovation in Brain Science (CIBS)

The Center for Innovation in Brain Science (CIBS) is seeking a research scientist to lead and implement data-driven research focused on advancing Precision Menopausal Hormone Therapy (P-MHT) for women worldwide. Integrate and analyze large-scale human datasets, including electronic health records, claims data, and omics profiles, to identify treatment response patterns, subtypes, and critical intervention windows that reduce Alzheimer's Disease (AD) risk. This position will involve applying 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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• Lead the 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.
• Provide mentorship and guidance to graduate and undergraduate students.
• Work collaboratively with other members and laboratories in CIBS.

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.

• Bachelor's degree in Neuroscience.
• 5 years of relevant work experience, or equivalent combination of education and work experience.

• 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.