Tenure-Track Faculty Positions in Artificial Intelligence and Machine Learning for Drug Discovery

Job Details

College of Pharmacy, Life Sciences Institute, and Medical School, University of Michigan, Ann Arbor, Michigan

The University of Michigan (U-M) invites applications for three tenure-track faculty positions in the area of Artificial Intelligence (AI) and Machine Learning (ML) in Drug Discovery. This is a unique cluster hire initiative spanning the College of Pharmacy, Life Sciences Institute (LSI), and Medical School, with support from the Office of the Provost. We are particularly seeking mid-career candidates who would meet University of Michigan criteria for appointment as associate professor or professor with tenure, and who have strong records of research excellence in AI/ML-driven approaches to drug discovery. Successful candidates will be appointed in the unit most aligned with their expertise, with the expectation of fostering interdisciplinary collaborations across the university. Joint appointments may be considered on a case-by-case basis. The successful candidates may also take a leadership role in the newly launched Institute for AI-Driven Therapeutics Discovery (AI-Tx), which received support from the University of Michigan Impact Institutes Initiative.

Successful candidates will be appointed within the unit most appropriate to their expertise while fostering interdisciplinary collaborations across the university.

All application materials should be submitted through the Interfolio Portal: https://apply.interfolio.com/174339

Strategic Impact and Vision

Drug development faces significant challenges, including high costs, long timelines, and a 90% failure rate in clinical trials. AI and ML have the potential to enhance drug discovery by improving the identification of disease and drug targets, accelerating the identification of drug candidates, optimizing the design of therapeutics, and guiding predictions of clinical outcomes. The goal of this cluster hire is to advance U-M’s leadership in drug discovery by integrating cutting-edge AI and ML methodologies into the drug discovery process, enhancing efficiency, reducing failure rates, and supporting therapeutic innovation.

This cluster hire aligns with U-M’s Look to Michigan strategic plan, emphasizing:

• Research Innovation: Advancing AI/ML methodologies for drug discovery and improving therapeutic success rates.
• Interdisciplinary Collaboration: Strengthening connections between computational and experimental drug development experts.
• Economic and Societal Impact: Translating discoveries into startup ventures and industry partnerships to drive drug commercialization.
• Education and Workforce Development: Training the next generation of scientists in AI/ML-enabled drug development.

Responsibilities

• Develop and sustain an externally funded research program in AI/ML-driven drug discovery.
• Publish high-impact research in leading scientific journals.
• Teach and mentor students and trainees across all learning and development stages.
• Collaborate with faculty across U-M to drive AI/ML applications in drug development.
• Engage with industry and government agencies to secure funding and foster translational research efforts.
• Contribute to the development of a new AI/ML-driven drug discovery center, integrating efforts across the College of Pharmacy, LSI, and Medical School, and other units in the University of Michigan.
• Contribute to the service missions of the department, university, and profession.
• The successful candidates may take a leadership role in the newly launched Institute of AI-Driven Therapeutics Discovery (AI-Tx).

Resources and Collaborative Environment

U-M provides an exceptionally collaborative and resource-rich environment for AI/ML and drug discovery research, including:

• Institute of AI-driven therapeutics discovery (AI-Tx). UM just launched AI-Tx with a goal to integrate AI and machine learning to address root causes of drug development failures, aiming to revolutionize the discovery of small molecules and biologics and position UM as a global leader in this field.
• Michigan Drug Discovery (MDD): A hub for academic-industry partnerships, drug screening, medicinal chemistry, and translational research.
• Broad Campus Collaboration: A highly collaborative network of faculty from departments and Colleges, including the Department of Pharmacology, Computational Medicine and Bioinformatics, Michigan Institute for Data Sciences, College of Literature, Sciences, and the Arts, and College of Engineering.
• Core Facilities: High-throughput screening, medicinal chemistry, structural biology, cryo-electron microscopy, pharmacokinetics, bioinformatics, and AI-driven data analytics.
• Innovation and Commercialization Support: Access to incubator space, business mentoring, venture funding, and technology licensing through Innovation Partnerships.
• AI & Digital Health Innovation: A Presidential initiative providing deidentified multimodal health data, genetic data, data storage and processing, and research implementation services.
• e-HAIL Initiative: A collaboration between Michigan Medicine and the College of Engineering, advancing AI in healthcare and biomedical research.
• Newly Established U-M and Los Alamos National Laboratory Partnership: A strategic collaboration providing additional computational and experimental resources.

Qualifications

• Ph.D., M.D., or equivalent degree in pharmaceutical sciences, medicinal chemistry, pharmacology, computational biology, biomedical informatics, chemical engineering, bioinformatics, computer science, or a related field.
• Demonstrated excellence in research with a strong record of peer-reviewed publications and competitive funding, or the potential for building an independent externally funded program and/or contribute to larger scale grant submissions.
• Expertise in applying AI/ML methodologies to drug discovery, pharmacology, chemistry, bioinformatics, and/or computational biology.
• A commitment to teaching, mentoring, and training students and postdoctoral fellows in AI/ML-driven drug discovery.
• Demonstrated interest in interdisciplinary collaboration and contributing to drug discovery and therapeutic innovation.

All appointments will be made at the associate professor or full professor level with tenure. Eligible applicants include:

• Associate professors with tenure (or equivalent) at their current institution.
• Newly promoted full professors with tenure at their current institution.
• Assistant professors in their 4th–6th year who demonstrate a record consistent with the University of Michigan's criteria for promotion to associate professor with tenure. Candidates should show a strong and independent scholarly trajectory with evidence of national or international recognition, along with effective teaching and meaningful service contributions.

Application Instructions

Application deadline July 1, 2026.

To apply, please submit the following materials:

• Cover letter specifying the preferred tenure home unit (College of Pharmacy or Medical School) and how their expertise aligns with the AI/ML drug discovery focus areas.
• Curriculum vitae.
• Statement of research interests and vision (2–3 pages).
• Statement of teaching philosophy and mentoring approach (1–2 pages).
• Names and contact information for five-eight arms length references.

All application materials should be submitted through the Interfolio Portal: https://apply.interfolio.com/174339

For informal inquiries, please contact the search committee chair, Dr. Duxin Sun (duxins@umich.edu)