Research Assistant Professor, Venture Methodology

Role Summary

Eshelman Innovation, housed in the nation's #1 pharmacy school, was created to address translational challenges with a game-changing new approach to academic innovation and commercialization. Through its venture studio, Eshelman Innovation has built Kairos, a venture creation methodology that pairs faculty innovators with entrepreneurial student talent, and Kalphai, the agentic AI platform that encodes that methodology and runs the studio end to end. Eshelman Innovation seeks a dynamic leader to formalize, validate, and scale the Kairos methodology as a licensable asset deployed across academic medical centers and university innovation organizations.

The chosen candidate will serve as the Principal Investigator for the methodology research program and will focus on generating the outcomes evidence that establishes Kairos as the defensible standard for AI-enabled venture creation, strengthening its commercial position and accelerating its adoption by partner institutions. This position provides an opportunity to change how universities convert research into companies, at a moment when the tools to do so are being defined.

Minimum Education and Experience Requirements

Ph.D. in strategy, entrepreneurship, innovation management, information systems, organizational behavior, health services research, science and technology policy, economics, education research, sociology, or a related field. Candidates whose doctoral training lies outside these fields but who bring directly relevant research experience are encouraged to apply.

Preferred Qualifications, Competencies, and Experience

• Rigorous empirical research training and the ability to design defensible studies in real-world settings rather than controlled ones, including familiarity with measurement, construct validity, and the limits of causal claims from field data.
• Demonstrated interest in entrepreneurship, venture creation, technology commercialization, innovation systems, or how organizations make decisions under uncertainty.
• Sufficient working fluency with generative AI tools to reason about what an agentic system is doing and evaluate the quality of its judgments; software development experience is not required, and comfort with analysis in Python, R, or comparable tools is sufficient.
• Experience working with messy operational data or designing measurement into a live system.
• Direct exposure to venture studios, accelerators, incubators, university technology transfer, or early-stage investing, in any capacity, whether as researcher, practitioner, advisor, or participant.
• Familiarity with design-science, action-research, implementation-science, or other methodologies suited to studying a system while helping build it.
• Domain knowledge in digital health, health services, life sciences commercialization, or health system adoption and reimbursement dynamics.
• Experience coordinating research across multiple sites or institutions.
• A record of translating research findings into tools, curricula, playbooks, or product requirements that other people actually use.
• Experience leading or contributing to complex collaborations across research and operational teams, including mentoring students or junior colleagues.
• Excellent written and verbal communication, including publishing in respected refereed journals and presenting to varied academic, institutional, and commercial audiences.