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

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:

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.

• 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).

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…