We seek a highly motivated Research Associate with strong foundations in both theoretical artificial intelligence/machine learning (AI/ML) and applied data-driven research, particularly in health and biomedical domains. The position is for a 1–2-year paid fellowship working under the mentorship of Dr. Hojjat Salehinejad. The ideal candidate will demonstrate deep expertise in the mathematical and algorithmic principles underlying modern AI systems, as well as the ability to translate these methods into impactful real-world solutions to improve patient outcomes. Required Qualifications The candidate must possess strong expertise in: • Data structures and algorithms, with rigorous understanding of computational complexity and optimization • Stochastic processes, statistical learning theory, and machine learning, including deep learning • Theoretical foundations of AI/ML, such as probabilistic modeling, generalization theory, optimization methods, and representation learning • Advanced programming in Python, including scientific computing and ML libraries (e.g., Pandas, Scikit-learn) • Deep learning frameworks such as TensorFlow and/or PyTorch • Linux environments and shell scripting • Database systems (e.g., MySQL, Redis) • Containerization technologies (e.g., Docker) Experience with Google Cloud Platform (GCP) or other large-scale cloud computing environments is considered a plus. The candidate must have a strong track record of peer-reviewed publications demonstrating both methodological rigor and applied impact. Excellent proficiency in all four English language skills is required, with particular strength in scientific writing and manuscript preparation. Role and Responsibilities The Research Associate will work independently and lead research initiatives at the intersection of AI/ML methodology and healthcare transformation. Responsibilities include: • Designing and developing novel AI/ML algorithms grounded in solid theoretical principles • Translating advanced computational methods into deployable solutions for healthcare systems • Leading interdisciplinary collaborations • Publishing high-impact scientific work • Contributing to health services research (HSR), health policy, and quantitative health sciences Research activities may intersect with different clinical departments across the Mayo Clinic where advanced computational and statistical modeling approaches are required. The successful candidate will combine strong theoretical depth with demonstrated ability to implement scalable, clinically meaningful AI systems that improve patient care and health outcomes. Must have a Ph.D., M.D., or equivalent doctoral degree in a field deemed relevant by the program.