The Department of Mathematics in the College of Arts and Sciences at Howard University invites applications for a full-time, tenure-track Assistant or Associate Professor position. The department seeks candidates with research expertise and teaching interests in mathematics enhanced artificial intelligence (AI), with applications to radiology, endocrinology, and related biomedical domains. The anticipated start date is August 2026.

This position is part of the Provost’s Artificial Intelligence Cluster Hire Initiative and is embedded within the interdisciplinary cluster “Advancing Health through Mathematics-Enhanced AI for Radiology and Endocrinology”, jointly led by the College of Arts and Sciences and the College of Medicine. The cluster aims to strengthen university-wide research capacity by recruiting faculty whose scholarship advances mathematical foundations, modeling, and computational methodologies that support AI-driven biomedical discovery, while fostering collaboration across disciplines.

SUPERVISORY AUTHORITY

The successful candidate is expected to demonstrate a strong commitment to excellence in teaching, mentoring, and service. The faculty member will contribute to the growth of the Department of Mathematics’ research and academic programs in data science, applied mathematics, and AI, and will actively participate in interdisciplinary initiatives across the university.

NATURE AND SCOPE

From a mathematics-centered perspective, this position emphasizes the development and application of mathematical theory, statistical modeling, and computational methods that underpin modern AI and machine learning approaches in biomedical sciences. The cluster seeks to advance quantitative frameworks for imaging, inference, prediction, and decision-making in complex biological and clinical systems. The successful candidate will contribute to an interdisciplinary academic environment through research, teaching, mentorship, and collaboration. Responsibilities include leading independent and collaborative research programs at the interface of mathematics, AI, and biomedical imaging; contributing to undergraduate and graduate curriculum development; mentoring students; and conducting research that addresses health disparities and promotes inclusive excellence.

PRINCIPAL ACCOUNTABILITIES

• Conduct high-quality, externally visible research in applied mathematics, machine learning, and artificial intelligence, with relevance to biology and medicine.
• Develop and sustain an externally funded research program, including collaborative interdisciplinary projects.
• Teach undergraduate and graduate courses in mathematics, data science, machine learning, and artificial intelligence.
• Supervise and mentor undergraduate and graduate students, postdoctoral scholars, and junior researchers.
• Collaborate with faculty across mathematics, medicine, and related disciplines to advance AI-enabled quantitative methods for biomedical applications.

CORE COMPETENCIES

The Department of Mathematics particularly encourages applicants with expertise in one or more of the following areas:

• Mathematical Foundations of AI and Machine Learning: Optimization, statistical learning theory, inverse problems, uncertainty quantification, dynamical systems, or computational mathematics relevant to AI.
• AI and Data-Driven Modeling in Biomedicine: Development of mathematical and computational methods for imaging, neuroscience, endocrinology, diabetes, or other data-intensive biomedical domains.
• Interdisciplinary Research and Collaboration: Demonstrated ability to collaborate with clinicians, biologists, or engineers to develop mathematically grounded AI/ML approaches for complex biological and medical datasets.

MINIMUM REQUIREMENTS

• Ph.D. in Mathematics, Applied Mathematics, Computer Science, Data Science, Artificial Intelligence, or a closely related field.
• Strong research record in mathematics-driven AI or ML, with applications to biology or medicine, evidenced by peer-reviewed publications.
• Demonstrated ability or clear potential to secure external research funding.
• Evidence of excellence or strong potential in undergraduate and graduate teaching.
• Commitment to interdisciplinary collaboration, student mentorship, and advancing diversity, equity, and inclusion in teaching and research.